import os
import pandas as pd
import numpy as np
from datetime import datetime
import glob
import shutil
import sqlite3
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
from sklearn.pipeline import make_pipeline
import scipy.stats as stats
from scipy.stats import ttest_ind
from scipy.stats import f_oneway
from scipy.stats import chi2_contingency
from statsmodels.stats.proportion import proportions_ztest
from scipy.stats import ttest_1samp
from statsmodels.stats.multicomp import pairwise_tukeyhsd
from scipy.stats import pointbiserialr
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LinearRegression
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import make_column_transformer
from mpl_toolkits.mplot3d import Axes3D
from statsmodels.formula.api import ols
import statsmodels.api as sm
from sklearn import linear_model
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
os.getcwd()
'/Users/michaelbyrd/Desktop/MIS556'
# Specify the path of the directory you want to change to
new_directory = "/Users/michaelbyrd/Desktop/MIS556/stats"
# Change the current working directory
os.chdir(new_directory)
# Display Directory
os.getcwd()
'/Users/michaelbyrd/Desktop/MIS556/stats'
# Display All Columns
pd.set_option('display.max_columns', None)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)
# Display All Columns
pd.set_option('display.max_columns', None)
# Function for Complete Breakdown
def breakdown(df):
# print("Head", "\n\n\n", df.head())
# print("Tail", "\n\n\n", df.tail())
# print("\n\n\n", "Columns", "\n", df.columns)
# print("\n\n\n", "Describe", "\n", df.describe())
# print("\n\n\n", "Correlation Matrix", "\n", df.corr())
# print("\n\n\n", "Info", "\n", df.info())
print("Distinct Count", "\n", df.nunique())
print("\n\n\n", "Count", "\n", df.count())
print("\n\n\n", "DTypes", "\n", df.dtypes)
print("\n\n\n", "Shape", "\n", df.shape)
print("\n\n\n", "Nulls", "\n", df.isna().sum())
print("\n\n\n", "Null %", "\n", round(df.isna().sum()/len(df)*100, 2))
# print("\n\n\n", "Mean", "\n\n\n", df.mean())
# print("\n\n\n", "Median", "\n\n\n", df.median())
# print("\n\n\n", "Standard Deviation", "\n\n\n", df.std())
# print("\n\n\n", "Max", "\n\n\n", df.max())
# print("\n\n\n", "Min", "\n\n\n", df.min())
# Function to convert time format to float representing minutes
def convert_to_minutes(time_str):
if isinstance(time_str, float):
return time_str # Return the float value directly
else:
minutes, seconds = map(int, time_str.split(':'))
return minutes + seconds / 60.0
def find_duplicates_in_column(df, column_name):
"""
Find and display a list of duplicates in a specified column of a DataFrame.
Parameters:
- df: DataFrame
The DataFrame containing the column with potential duplicates.
- column_name: str
The name of the column to search for duplicates.
Returns:
- list
A list containing unique duplicate values found in the specified column.
"""
# Find duplicates in the specified column
duplicate_values = df[df.duplicated(subset=[column_name], keep=False)]
# Extract the list of duplicate values
duplicate_list = duplicate_values[column_name].unique().tolist()
return duplicate_list
def convert_inches(height):
"""
Convert height from feet'inches" format to inches.
Parameters:
- height: str
Height value in the format feet'inches" (e.g., "5'10").
Returns:
- int
Height in inches.
"""
if pd.isna(height): # Handling NaN values
return height
feet, inches = height.split("'")
total_inches = int(feet) * 12 + int(inches.strip('"'))
return total_inches
def unique_column_values(dataframe, column_name):
"""
Returns a list of unique values in the specified column of a DataFrame.
Args:
- dataframe: pandas DataFrame containing the data
- column_name: name of the column
Returns:
- unique_values: list of unique values in the specified column
"""
unique_values = dataframe[column_name].unique().tolist()
return unique_values
# Define the database file name
db_file = 'mma.db'
# Connect to the SQLite database
conn = sqlite3.connect(db_file)
# Create a cursor object to execute SQL queries
cursor = conn.cursor()
# Execute SQL query to get all table names in the database
cursor.execute("SELECT name FROM sqlite_master WHERE type='table';")
# Fetch all the table names
table_names = cursor.fetchall()
# Print the table names
print("Tables in the database:")
for name in table_names:
print(name[0])
Tables in the database: master fighter_buyrate event_buyrate frame career fight_average round_average records heavyweight light_heavyweight middleweight welterweight lightweight featherweight bantamweight flyweight women_featherweight women_bantamweight women_flyweight women_strawweight
len(table_names)
20
connection = sqlite3.connect(db_file)
master = pd.read_sql('SELECT * FROM master', connection)
master
| EVENT | BOUT | ROUND | FIGHTER | KD | TD | SUB.ATT | REV | CTRL | HEAD | BODY | LEG | DISTANCE | CLINCH | GROUND | SIG | TOT.SIG | SIG_% | STR | TOTAL.STR | TOT_% | TOTAL.TD | TD_% | TOTAL.HEAD | HEAD_% | TOTAL.BODY | BODY_% | TOTAL.LEG | LEG_% | TOTAL.DISTANCE | DISTANCE_% | TOTAL.CLINCH | CLINCH_% | TOTAL.GROUND | GROUND_% | HEIGHT | WEIGHT | REACH | STANCE | DOB | APE_INDEX | FRAME | FIRST | LAST | NICKNAME | OUTCOME | WEIGHTCLASS | METHOD | FINISH_ROUND | TIME | TIME_FORMAT | REFEREE | DETAILS | TITLE | GENDER | DATE | LOCATION | CITY | STATE | COUNTRY | Gate | Buyrate | RESULT | AGE | OPP | HEIGHT_AVG | REACH_AVG | AGE_AVG | AI_AVG | FRAME_AVG | HEIGHT_DIFF | REACH_DIFF | AGE_DIFF | AI_DIFF | FRAME_DIFF | KEY | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | OPP_EVENT | OPP_BOUT | OPP_ROUND | OPP_FIGHTER | OPP_KD | OPP_TD | OPP_SUB.ATT | OPP_REV | OPP_CTRL | OPP_HEAD | OPP_BODY | OPP_LEG | OPP_DISTANCE | OPP_CLINCH | OPP_GROUND | OPP_SIG | OPP_TOT.SIG | OPP_SIG_% | OPP_STR | OPP_TOTAL.STR | OPP_TOT_% | OPP_TOTAL.TD | OPP_TD_% | OPP_TOTAL.HEAD | OPP_HEAD_% | OPP_TOTAL.BODY | OPP_BODY_% | OPP_TOTAL.LEG | OPP_LEG_% | OPP_TOTAL.DISTANCE | OPP_DISTANCE_% | OPP_TOTAL.CLINCH | OPP_CLINCH_% | OPP_TOTAL.GROUND | OPP_GROUND_% | OPP_HEIGHT | OPP_WEIGHT | OPP_REACH | OPP_STANCE | OPP_DOB | OPP_APE_INDEX | OPP_FRAME | OPP_FIRST | OPP_LAST | OPP_NICKNAME | OPP_OUTCOME | OPP_WEIGHTCLASS | OPP_METHOD | OPP_FINISH_ROUND | OPP_TIME | OPP_TIME_FORMAT | OPP_REFEREE | OPP_DETAILS | OPP_TITLE | OPP_GENDER | OPP_DATE | OPP_LOCATION | OPP_CITY | OPP_STATE | OPP_COUNTRY | OPP_Gate | OPP_Buyrate | OPP_RESULT | OPP_AGE | OPP_OPP | OPP_HEIGHT_AVG | OPP_REACH_AVG | OPP_AGE_AVG | OPP_AI_AVG | OPP_FRAME_AVG | OPP_HEIGHT_DIFF | OPP_REACH_DIFF | OPP_AGE_DIFF | OPP_AI_DIFF | OPP_FRAME_DIFF | OPP_KEY | OPP_EVER_CHAMPION | OPP_EVER_INTERIM | OPP_EVER_CHALLENGER | HEIGHT_DELTA | REACH_DELTA | AGE_DELTA | AI_DELTA | FRAME_DELTA | TD_DEF | TD_DEF% | HEAD_DEF | HEAD_DEF% | BODY_DEF | BODY_DEF% | LEG_DEF | LEG_DEF% | DISTANCE_DEF | DISTANCE_DEF% | CLINCH_DEF | CLINCH_DEF% | GROUND_DEF | GROUND_DEF% | SIG_DEF | SIG_DEF% | STR_DEF | STR_DEF% | FIGHT_ID | METHOD1 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | UFC 299: O'Malley vs. Vera 2 | CJ Vergara vs. Asu Almabayev | 1.0 | Asu Almabayev | 0.0 | 4.0 | 0.0 | 0.0 | 3.233333 | 7.0 | 3.0 | 6.0 | 10.0 | 2.0 | 4.0 | 16.0 | 25.0 | 0.640000 | 23.0 | 34.0 | 0.676471 | 6.0 | 0.666667 | 15.0 | 0.466667 | 4.0 | 0.750000 | 6.0 | 1.0 | 19.0 | 0.526316 | 2.0 | 1.000000 | 4.0 | 1.0 | NaN | NaN | NaN | None | None | NaN | NaN | None | None | None | L/W | Flyweight | Decision - Unanimous | 3.0 | 5.0 | 3 Rnd (5-5-5) | Keith Peterson | David Tirelli 27 - 30.MichaelTate 27 - 30.Sal ... | None | Men's | 2024-03-09 | Miami, Florida, USA | Miami | Florida | USA | 14142904.0 | NaN | Win | NaN | CJ Vergara | 65.577530 | 66.875164 | 28.567909 | 1.019928 | 66.226347 | NaN | NaN | NaN | NaN | NaN | UFC299:O'Malleyvs.Vera2-CJVergaravs.AsuAlmabay... | No | No | No | UFC 299: O'Malley vs. Vera 2 | CJ Vergara vs. Asu Almabayev | 1.0 | CJ Vergara | 0.0 | 0.0 | 0.0 | 0.0 | 0.233333 | 3.0 | 3.0 | 1.0 | 4.0 | 3.0 | 0.0 | 7.0 | 14.0 | 0.500000 | 13.0 | 20.0 | 0.650000 | 0.0 | NaN | 9.0 | 0.333333 | 3.0 | 1.000000 | 2.0 | 0.5 | 11.0 | 0.363636 | 3.0 | 1.000000 | 0.0 | NaN | 66.0 | 135.0 | 68.0 | Orthodox | 1991-06-18 | 1.030303 | 67.0 | CJ | Vergara | None | L/W | Flyweight | Decision - Unanimous | 3.0 | 5.0 | 3 Rnd (5-5-5) | Keith Peterson | David Tirelli 27 - 30.MichaelTate 27 - 30.Sal ... | None | Men's | 2024-03-09 | Miami, Florida, USA | Miami | Florida | USA | 14142904.0 | NaN | Loss | 32.0 | Asu Almabayev | 65.577530 | 66.875164 | 28.567909 | 1.019928 | 66.226347 | 0.422470 | 1.124836 | 3.432091 | 0.010375 | 0.773653 | UFC299:O'Malleyvs.Vera2-CJVergaravs.AsuAlmabay... | No | No | No | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | 6.0 | 0.666667 | 0.0 | 0.000000 | 1.0 | 0.5 | 7.0 | 0.636364 | 0.0 | 0.000000 | 0.0 | NaN | 7.0 | 0.500000 | 7.0 | 0.350000 | UFC 299: O'Malley vs. Vera 2-CJ Vergara vs. As... | Decision |
| 1 | UFC 299: O'Malley vs. Vera 2 | CJ Vergara vs. Asu Almabayev | 2.0 | Asu Almabayev | 0.0 | 2.0 | 0.0 | 0.0 | 3.583333 | 10.0 | 1.0 | 1.0 | 4.0 | 1.0 | 7.0 | 12.0 | 21.0 | 0.571429 | 36.0 | 55.0 | 0.654545 | 2.0 | 1.000000 | 19.0 | 0.526316 | 1.0 | 1.000000 | 1.0 | 1.0 | 9.0 | 0.444444 | 2.0 | 0.500000 | 10.0 | 0.7 | NaN | NaN | NaN | None | None | NaN | NaN | None | None | None | L/W | Flyweight | Decision - Unanimous | 3.0 | 5.0 | 3 Rnd (5-5-5) | Keith Peterson | David Tirelli 27 - 30.MichaelTate 27 - 30.Sal ... | None | Men's | 2024-03-09 | Miami, Florida, USA | Miami | Florida | USA | 14142904.0 | NaN | Win | NaN | CJ Vergara | 65.577530 | 66.875164 | 28.567909 | 1.019928 | 66.226347 | NaN | NaN | NaN | NaN | NaN | UFC299:O'Malleyvs.Vera2-CJVergaravs.AsuAlmabay... | No | No | No | UFC 299: O'Malley vs. Vera 2 | CJ Vergara vs. Asu Almabayev | 2.0 | CJ Vergara | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 5.0 | 2.0 | 1.0 | 5.0 | 0.0 | 3.0 | 8.0 | 12.0 | 0.666667 | 25.0 | 30.0 | 0.833333 | 0.0 | NaN | 8.0 | 0.625000 | 3.0 | 0.666667 | 1.0 | 1.0 | 9.0 | 0.555556 | 0.0 | NaN | 3.0 | 1.0 | 66.0 | 135.0 | 68.0 | Orthodox | 1991-06-18 | 1.030303 | 67.0 | CJ | Vergara | None | L/W | Flyweight | Decision - Unanimous | 3.0 | 5.0 | 3 Rnd (5-5-5) | Keith Peterson | David Tirelli 27 - 30.MichaelTate 27 - 30.Sal ... | None | Men's | 2024-03-09 | Miami, Florida, USA | Miami | Florida | USA | 14142904.0 | NaN | Loss | 32.0 | Asu Almabayev | 65.577530 | 66.875164 | 28.567909 | 1.019928 | 66.226347 | 0.422470 | 1.124836 | 3.432091 | 0.010375 | 0.773653 | UFC299:O'Malleyvs.Vera2-CJVergaravs.AsuAlmabay... | No | No | No | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | 3.0 | 0.375000 | 1.0 | 0.333333 | 0.0 | 0.0 | 4.0 | 0.444444 | 0.0 | NaN | 0.0 | 0.0 | 4.0 | 0.333333 | 5.0 | 0.166667 | UFC 299: O'Malley vs. Vera 2-CJ Vergara vs. As... | Decision |
| 2 | UFC 299: O'Malley vs. Vera 2 | CJ Vergara vs. Asu Almabayev | 3.0 | Asu Almabayev | 0.0 | 3.0 | 0.0 | 0.0 | 2.716667 | 5.0 | 5.0 | 6.0 | 9.0 | 5.0 | 2.0 | 16.0 | 31.0 | 0.516129 | 26.0 | 43.0 | 0.604651 | 6.0 | 0.500000 | 19.0 | 0.263158 | 6.0 | 0.833333 | 6.0 | 1.0 | 23.0 | 0.391304 | 6.0 | 0.833333 | 2.0 | 1.0 | NaN | NaN | NaN | None | None | NaN | NaN | None | None | None | L/W | Flyweight | Decision - Unanimous | 3.0 | 5.0 | 3 Rnd (5-5-5) | Keith Peterson | David Tirelli 27 - 30.MichaelTate 27 - 30.Sal ... | None | Men's | 2024-03-09 | Miami, Florida, USA | Miami | Florida | USA | 14142904.0 | NaN | Win | NaN | CJ Vergara | 65.577530 | 66.875164 | 28.567909 | 1.019928 | 66.226347 | NaN | NaN | NaN | NaN | NaN | UFC299:O'Malleyvs.Vera2-CJVergaravs.AsuAlmabay... | No | No | No | UFC 299: O'Malley vs. Vera 2 | CJ Vergara vs. Asu Almabayev | 3.0 | CJ Vergara | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 5.0 | 9.0 | 0.0 | 13.0 | 1.0 | 0.0 | 14.0 | 27.0 | 0.518519 | 28.0 | 42.0 | 0.666667 | 0.0 | NaN | 15.0 | 0.333333 | 11.0 | 0.818182 | 1.0 | 0.0 | 26.0 | 0.500000 | 1.0 | 1.000000 | 0.0 | NaN | 66.0 | 135.0 | 68.0 | Orthodox | 1991-06-18 | 1.030303 | 67.0 | CJ | Vergara | None | L/W | Flyweight | Decision - Unanimous | 3.0 | 5.0 | 3 Rnd (5-5-5) | Keith Peterson | David Tirelli 27 - 30.MichaelTate 27 - 30.Sal ... | None | Men's | 2024-03-09 | Miami, Florida, USA | Miami | Florida | USA | 14142904.0 | NaN | Loss | 32.0 | Asu Almabayev | 65.577530 | 66.875164 | 28.567909 | 1.019928 | 66.226347 | 0.422470 | 1.124836 | 3.432091 | 0.010375 | 0.773653 | UFC299:O'Malleyvs.Vera2-CJVergaravs.AsuAlmabay... | No | No | No | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | 10.0 | 0.666667 | 2.0 | 0.181818 | 1.0 | 1.0 | 13.0 | 0.500000 | 0.0 | 0.000000 | 0.0 | NaN | 13.0 | 0.481481 | 14.0 | 0.333333 | UFC 299: O'Malley vs. Vera 2-CJ Vergara vs. As... | Decision |
| 3 | UFC 299: O'Malley vs. Vera 2 | CJ Vergara vs. Asu Almabayev | 1.0 | CJ Vergara | 0.0 | 0.0 | 0.0 | 0.0 | 0.233333 | 3.0 | 3.0 | 1.0 | 4.0 | 3.0 | 0.0 | 7.0 | 14.0 | 0.500000 | 13.0 | 20.0 | 0.650000 | 0.0 | NaN | 9.0 | 0.333333 | 3.0 | 1.000000 | 2.0 | 0.5 | 11.0 | 0.363636 | 3.0 | 1.000000 | 0.0 | NaN | 66.0 | 135.0 | 68.0 | Orthodox | 1991-06-18 | 1.030303 | 67.0 | CJ | Vergara | None | L/W | Flyweight | Decision - Unanimous | 3.0 | 5.0 | 3 Rnd (5-5-5) | Keith Peterson | David Tirelli 27 - 30.MichaelTate 27 - 30.Sal ... | None | Men's | 2024-03-09 | Miami, Florida, USA | Miami | Florida | USA | 14142904.0 | NaN | Loss | 32.0 | Asu Almabayev | 65.577530 | 66.875164 | 28.567909 | 1.019928 | 66.226347 | 0.422470 | 1.124836 | 3.432091 | 0.010375 | 0.773653 | UFC299:O'Malleyvs.Vera2-CJVergaravs.AsuAlmabay... | No | No | No | UFC 299: O'Malley vs. Vera 2 | CJ Vergara vs. Asu Almabayev | 1.0 | Asu Almabayev | 0.0 | 4.0 | 0.0 | 0.0 | 3.233333 | 7.0 | 3.0 | 6.0 | 10.0 | 2.0 | 4.0 | 16.0 | 25.0 | 0.640000 | 23.0 | 34.0 | 0.676471 | 6.0 | 0.666667 | 15.0 | 0.466667 | 4.0 | 0.750000 | 6.0 | 1.0 | 19.0 | 0.526316 | 2.0 | 1.000000 | 4.0 | 1.0 | NaN | NaN | NaN | None | None | NaN | NaN | None | None | None | L/W | Flyweight | Decision - Unanimous | 3.0 | 5.0 | 3 Rnd (5-5-5) | Keith Peterson | David Tirelli 27 - 30.MichaelTate 27 - 30.Sal ... | None | Men's | 2024-03-09 | Miami, Florida, USA | Miami | Florida | USA | 14142904.0 | NaN | Win | NaN | CJ Vergara | 65.577530 | 66.875164 | 28.567909 | 1.019928 | 66.226347 | NaN | NaN | NaN | NaN | NaN | UFC299:O'Malleyvs.Vera2-CJVergaravs.AsuAlmabay... | No | No | No | NaN | NaN | NaN | NaN | NaN | 2.0 | 0.333333 | 8.0 | 0.533333 | 1.0 | 0.250000 | 0.0 | 0.0 | 9.0 | 0.473684 | 0.0 | 0.000000 | 0.0 | 0.0 | 9.0 | 0.360000 | 11.0 | 0.323529 | UFC 299: O'Malley vs. Vera 2-CJ Vergara vs. As... | Decision |
| 4 | UFC 299: O'Malley vs. Vera 2 | CJ Vergara vs. Asu Almabayev | 2.0 | CJ Vergara | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 5.0 | 2.0 | 1.0 | 5.0 | 0.0 | 3.0 | 8.0 | 12.0 | 0.666667 | 25.0 | 30.0 | 0.833333 | 0.0 | NaN | 8.0 | 0.625000 | 3.0 | 0.666667 | 1.0 | 1.0 | 9.0 | 0.555556 | 0.0 | NaN | 3.0 | 1.0 | 66.0 | 135.0 | 68.0 | Orthodox | 1991-06-18 | 1.030303 | 67.0 | CJ | Vergara | None | L/W | Flyweight | Decision - Unanimous | 3.0 | 5.0 | 3 Rnd (5-5-5) | Keith Peterson | David Tirelli 27 - 30.MichaelTate 27 - 30.Sal ... | None | Men's | 2024-03-09 | Miami, Florida, USA | Miami | Florida | USA | 14142904.0 | NaN | Loss | 32.0 | Asu Almabayev | 65.577530 | 66.875164 | 28.567909 | 1.019928 | 66.226347 | 0.422470 | 1.124836 | 3.432091 | 0.010375 | 0.773653 | UFC299:O'Malleyvs.Vera2-CJVergaravs.AsuAlmabay... | No | No | No | UFC 299: O'Malley vs. Vera 2 | CJ Vergara vs. Asu Almabayev | 2.0 | Asu Almabayev | 0.0 | 2.0 | 0.0 | 0.0 | 3.583333 | 10.0 | 1.0 | 1.0 | 4.0 | 1.0 | 7.0 | 12.0 | 21.0 | 0.571429 | 36.0 | 55.0 | 0.654545 | 2.0 | 1.000000 | 19.0 | 0.526316 | 1.0 | 1.000000 | 1.0 | 1.0 | 9.0 | 0.444444 | 2.0 | 0.500000 | 10.0 | 0.7 | NaN | NaN | NaN | None | None | NaN | NaN | None | None | None | L/W | Flyweight | Decision - Unanimous | 3.0 | 5.0 | 3 Rnd (5-5-5) | Keith Peterson | David Tirelli 27 - 30.MichaelTate 27 - 30.Sal ... | None | Men's | 2024-03-09 | Miami, Florida, USA | Miami | Florida | USA | 14142904.0 | NaN | Win | NaN | CJ Vergara | 65.577530 | 66.875164 | 28.567909 | 1.019928 | 66.226347 | NaN | NaN | NaN | NaN | NaN | UFC299:O'Malleyvs.Vera2-CJVergaravs.AsuAlmabay... | No | No | No | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.000000 | 9.0 | 0.473684 | 0.0 | 0.000000 | 0.0 | 0.0 | 5.0 | 0.555556 | 1.0 | 0.500000 | 3.0 | 0.3 | 9.0 | 0.428571 | 19.0 | 0.345455 | UFC 299: O'Malley vs. Vera 2-CJ Vergara vs. As... | Decision |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 34579 | UFC 26: Ultimate Field Of Dreams | Tyrone Roberts vs. David Dodd | 2.0 | David Dodd | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 1.0 | 2.0 | 0.0 | 0.0 | 2.0 | 1.0 | 3.0 | 5.0 | 0.600000 | 9.0 | 12.0 | 0.750000 | 0.0 | NaN | 3.0 | 0.333333 | 2.0 | 1.000000 | 0.0 | NaN | 2.0 | 0.000000 | 2.0 | 1.000000 | 1.0 | 1.0 | 74.0 | 200.0 | 74.0 | Orthodox | 1973-09-22 | 1.000000 | 74.0 | David | Dodd | None | W/L | Middleweight | Decision - Unanimous | 3.0 | 5.0 | 3 Rnd (5-5-5) | John McCarthy | None | None | Men's | 2000-06-09 | Cedar Rapids, Iowa, USA | Cedar Rapids | Iowa | USA | NaN | NaN | Loss | 26.0 | Tyrone Roberts | 72.668122 | 74.782579 | 30.365562 | 1.029232 | 73.725350 | 1.331878 | -0.782579 | -4.365562 | -0.029232 | 0.274650 | UFC26:UltimateFieldOfDreams-TyroneRobertsvs.Da... | No | No | No | UFC 26: Ultimate Field Of Dreams | Tyrone Roberts vs. David Dodd | 2.0 | Tyrone Roberts | 0.0 | 1.0 | 0.0 | 0.0 | 1.533333 | 3.0 | 1.0 | 0.0 | 2.0 | 2.0 | 0.0 | 4.0 | 14.0 | 0.285714 | 36.0 | 50.0 | 0.720000 | 1.0 | 1.000000 | 13.0 | 0.230769 | 1.0 | 1.000000 | 0.0 | NaN | 8.0 | 0.250000 | 6.0 | 0.333333 | 0.0 | NaN | 69.0 | 185.0 | 69.0 | Orthodox | None | 1.000000 | 69.0 | Tyrone | Roberts | Native Warrior | W/L | Middleweight | Decision - Unanimous | 3.0 | 5.0 | 3 Rnd (5-5-5) | John McCarthy | None | None | Men's | 2000-06-09 | Cedar Rapids, Iowa, USA | Cedar Rapids | Iowa | USA | NaN | NaN | Win | NaN | David Dodd | 72.668122 | 74.782579 | 30.365562 | 1.029232 | 73.725350 | -3.668122 | -5.782579 | NaN | -0.029232 | -4.725350 | UFC26:UltimateFieldOfDreams-TyroneRobertsvs.Da... | No | No | No | 5.0 | 5.0 | NaN | 0.0 | 5.0 | 0.0 | 0.000000 | 10.0 | 0.769231 | 0.0 | 0.000000 | 0.0 | NaN | 6.0 | 0.750000 | 4.0 | 0.666667 | 0.0 | NaN | 10.0 | 0.714286 | 14.0 | 0.280000 | UFC 26: Ultimate Field Of Dreams-Tyrone Robert... | Decision |
| 34580 | UFC 26: Ultimate Field Of Dreams | Tyrone Roberts vs. David Dodd | 3.0 | David Dodd | 0.0 | 0.0 | 0.0 | 0.0 | 0.033333 | 0.0 | 3.0 | 0.0 | 0.0 | 3.0 | 0.0 | 3.0 | 13.0 | 0.230769 | 3.0 | 13.0 | 0.230769 | 1.0 | 0.000000 | 9.0 | 0.000000 | 4.0 | 0.750000 | 0.0 | NaN | 9.0 | 0.000000 | 4.0 | 0.750000 | 0.0 | NaN | 74.0 | 200.0 | 74.0 | Orthodox | 1973-09-22 | 1.000000 | 74.0 | David | Dodd | None | W/L | Middleweight | Decision - Unanimous | 3.0 | 5.0 | 3 Rnd (5-5-5) | John McCarthy | None | None | Men's | 2000-06-09 | Cedar Rapids, Iowa, USA | Cedar Rapids | Iowa | USA | NaN | NaN | Loss | 26.0 | Tyrone Roberts | 72.668122 | 74.782579 | 30.365562 | 1.029232 | 73.725350 | 1.331878 | -0.782579 | -4.365562 | -0.029232 | 0.274650 | UFC26:UltimateFieldOfDreams-TyroneRobertsvs.Da... | No | No | No | UFC 26: Ultimate Field Of Dreams | Tyrone Roberts vs. David Dodd | 3.0 | Tyrone Roberts | 0.0 | 0.0 | 0.0 | 0.0 | 0.033333 | 5.0 | 1.0 | 0.0 | 4.0 | 2.0 | 0.0 | 6.0 | 30.0 | 0.200000 | 8.0 | 32.0 | 0.250000 | 0.0 | NaN | 27.0 | 0.185185 | 3.0 | 0.333333 | 0.0 | NaN | 23.0 | 0.173913 | 7.0 | 0.285714 | 0.0 | NaN | 69.0 | 185.0 | 69.0 | Orthodox | None | 1.000000 | 69.0 | Tyrone | Roberts | Native Warrior | W/L | Middleweight | Decision - Unanimous | 3.0 | 5.0 | 3 Rnd (5-5-5) | John McCarthy | None | None | Men's | 2000-06-09 | Cedar Rapids, Iowa, USA | Cedar Rapids | Iowa | USA | NaN | NaN | Win | NaN | David Dodd | 72.668122 | 74.782579 | 30.365562 | 1.029232 | 73.725350 | -3.668122 | -5.782579 | NaN | -0.029232 | -4.725350 | UFC26:UltimateFieldOfDreams-TyroneRobertsvs.Da... | No | No | No | 5.0 | 5.0 | NaN | 0.0 | 5.0 | 0.0 | NaN | 22.0 | 0.814815 | 2.0 | 0.666667 | 0.0 | NaN | 19.0 | 0.826087 | 5.0 | 0.714286 | 0.0 | NaN | 24.0 | 0.800000 | 24.0 | 0.750000 | UFC 26: Ultimate Field Of Dreams-Tyrone Robert... | Decision |
| 34581 | UFC 26: Ultimate Field Of Dreams | Tyrone Roberts vs. David Dodd | 1.0 | Tyrone Roberts | 0.0 | 0.0 | 0.0 | 0.0 | 3.216667 | 3.0 | 1.0 | 2.0 | 6.0 | 0.0 | 0.0 | 6.0 | 16.0 | 0.375000 | 61.0 | 81.0 | 0.753086 | 0.0 | NaN | 13.0 | 0.230769 | 1.0 | 1.000000 | 2.0 | 1.0 | 14.0 | 0.428571 | 0.0 | NaN | 2.0 | 0.0 | 69.0 | 185.0 | 69.0 | Orthodox | None | 1.000000 | 69.0 | Tyrone | Roberts | Native Warrior | W/L | Middleweight | Decision - Unanimous | 3.0 | 5.0 | 3 Rnd (5-5-5) | John McCarthy | None | None | Men's | 2000-06-09 | Cedar Rapids, Iowa, USA | Cedar Rapids | Iowa | USA | NaN | NaN | Win | NaN | David Dodd | 72.668122 | 74.782579 | 30.365562 | 1.029232 | 73.725350 | -3.668122 | -5.782579 | NaN | -0.029232 | -4.725350 | UFC26:UltimateFieldOfDreams-TyroneRobertsvs.Da... | No | No | No | UFC 26: Ultimate Field Of Dreams | Tyrone Roberts vs. David Dodd | 1.0 | David Dodd | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 4.0 | 0.0 | 0.0 | 3.0 | 0.0 | 1.0 | 4.0 | 13.0 | 0.307692 | 15.0 | 24.0 | 0.625000 | 1.0 | 0.000000 | 13.0 | 0.307692 | 0.0 | NaN | 0.0 | NaN | 11.0 | 0.272727 | 0.0 | NaN | 2.0 | 0.5 | 74.0 | 200.0 | 74.0 | Orthodox | 1973-09-22 | 1.000000 | 74.0 | David | Dodd | None | W/L | Middleweight | Decision - Unanimous | 3.0 | 5.0 | 3 Rnd (5-5-5) | John McCarthy | None | None | Men's | 2000-06-09 | Cedar Rapids, Iowa, USA | Cedar Rapids | Iowa | USA | NaN | NaN | Loss | 26.0 | Tyrone Roberts | 72.668122 | 74.782579 | 30.365562 | 1.029232 | 73.725350 | 1.331878 | -0.782579 | -4.365562 | -0.029232 | 0.274650 | UFC26:UltimateFieldOfDreams-TyroneRobertsvs.Da... | No | No | No | -5.0 | -5.0 | NaN | 0.0 | -5.0 | 1.0 | 1.000000 | 9.0 | 0.692308 | 0.0 | NaN | 0.0 | NaN | 8.0 | 0.727273 | 0.0 | NaN | 1.0 | 0.5 | 9.0 | 0.692308 | 9.0 | 0.375000 | UFC 26: Ultimate Field Of Dreams-Tyrone Robert... | Decision |
| 34582 | UFC 26: Ultimate Field Of Dreams | Tyrone Roberts vs. David Dodd | 2.0 | Tyrone Roberts | 0.0 | 1.0 | 0.0 | 0.0 | 1.533333 | 3.0 | 1.0 | 0.0 | 2.0 | 2.0 | 0.0 | 4.0 | 14.0 | 0.285714 | 36.0 | 50.0 | 0.720000 | 1.0 | 1.000000 | 13.0 | 0.230769 | 1.0 | 1.000000 | 0.0 | NaN | 8.0 | 0.250000 | 6.0 | 0.333333 | 0.0 | NaN | 69.0 | 185.0 | 69.0 | Orthodox | None | 1.000000 | 69.0 | Tyrone | Roberts | Native Warrior | W/L | Middleweight | Decision - Unanimous | 3.0 | 5.0 | 3 Rnd (5-5-5) | John McCarthy | None | None | Men's | 2000-06-09 | Cedar Rapids, Iowa, USA | Cedar Rapids | Iowa | USA | NaN | NaN | Win | NaN | David Dodd | 72.668122 | 74.782579 | 30.365562 | 1.029232 | 73.725350 | -3.668122 | -5.782579 | NaN | -0.029232 | -4.725350 | UFC26:UltimateFieldOfDreams-TyroneRobertsvs.Da... | No | No | No | UFC 26: Ultimate Field Of Dreams | Tyrone Roberts vs. David Dodd | 2.0 | David Dodd | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 1.0 | 2.0 | 0.0 | 0.0 | 2.0 | 1.0 | 3.0 | 5.0 | 0.600000 | 9.0 | 12.0 | 0.750000 | 0.0 | NaN | 3.0 | 0.333333 | 2.0 | 1.000000 | 0.0 | NaN | 2.0 | 0.000000 | 2.0 | 1.000000 | 1.0 | 1.0 | 74.0 | 200.0 | 74.0 | Orthodox | 1973-09-22 | 1.000000 | 74.0 | David | Dodd | None | W/L | Middleweight | Decision - Unanimous | 3.0 | 5.0 | 3 Rnd (5-5-5) | John McCarthy | None | None | Men's | 2000-06-09 | Cedar Rapids, Iowa, USA | Cedar Rapids | Iowa | USA | NaN | NaN | Loss | 26.0 | Tyrone Roberts | 72.668122 | 74.782579 | 30.365562 | 1.029232 | 73.725350 | 1.331878 | -0.782579 | -4.365562 | -0.029232 | 0.274650 | UFC26:UltimateFieldOfDreams-TyroneRobertsvs.Da... | No | No | No | -5.0 | -5.0 | NaN | 0.0 | -5.0 | 0.0 | NaN | 2.0 | 0.666667 | 0.0 | 0.000000 | 0.0 | NaN | 2.0 | 1.000000 | 0.0 | 0.000000 | 0.0 | 0.0 | 2.0 | 0.400000 | 3.0 | 0.250000 | UFC 26: Ultimate Field Of Dreams-Tyrone Robert... | Decision |
| 34583 | UFC 26: Ultimate Field Of Dreams | Tyrone Roberts vs. David Dodd | 3.0 | Tyrone Roberts | 0.0 | 0.0 | 0.0 | 0.0 | 0.033333 | 5.0 | 1.0 | 0.0 | 4.0 | 2.0 | 0.0 | 6.0 | 30.0 | 0.200000 | 8.0 | 32.0 | 0.250000 | 0.0 | NaN | 27.0 | 0.185185 | 3.0 | 0.333333 | 0.0 | NaN | 23.0 | 0.173913 | 7.0 | 0.285714 | 0.0 | NaN | 69.0 | 185.0 | 69.0 | Orthodox | None | 1.000000 | 69.0 | Tyrone | Roberts | Native Warrior | W/L | Middleweight | Decision - Unanimous | 3.0 | 5.0 | 3 Rnd (5-5-5) | John McCarthy | None | None | Men's | 2000-06-09 | Cedar Rapids, Iowa, USA | Cedar Rapids | Iowa | USA | NaN | NaN | Win | NaN | David Dodd | 72.668122 | 74.782579 | 30.365562 | 1.029232 | 73.725350 | -3.668122 | -5.782579 | NaN | -0.029232 | -4.725350 | UFC26:UltimateFieldOfDreams-TyroneRobertsvs.Da... | No | No | No | UFC 26: Ultimate Field Of Dreams | Tyrone Roberts vs. David Dodd | 3.0 | David Dodd | 0.0 | 0.0 | 0.0 | 0.0 | 0.033333 | 0.0 | 3.0 | 0.0 | 0.0 | 3.0 | 0.0 | 3.0 | 13.0 | 0.230769 | 3.0 | 13.0 | 0.230769 | 1.0 | 0.000000 | 9.0 | 0.000000 | 4.0 | 0.750000 | 0.0 | NaN | 9.0 | 0.000000 | 4.0 | 0.750000 | 0.0 | NaN | 74.0 | 200.0 | 74.0 | Orthodox | 1973-09-22 | 1.000000 | 74.0 | David | Dodd | None | W/L | Middleweight | Decision - Unanimous | 3.0 | 5.0 | 3 Rnd (5-5-5) | John McCarthy | None | None | Men's | 2000-06-09 | Cedar Rapids, Iowa, USA | Cedar Rapids | Iowa | USA | NaN | NaN | Loss | 26.0 | Tyrone Roberts | 72.668122 | 74.782579 | 30.365562 | 1.029232 | 73.725350 | 1.331878 | -0.782579 | -4.365562 | -0.029232 | 0.274650 | UFC26:UltimateFieldOfDreams-TyroneRobertsvs.Da... | No | No | No | -5.0 | -5.0 | NaN | 0.0 | -5.0 | 1.0 | 1.000000 | 9.0 | 1.000000 | 1.0 | 0.250000 | 0.0 | NaN | 9.0 | 1.000000 | 1.0 | 0.250000 | 0.0 | NaN | 10.0 | 0.769231 | 10.0 | 0.769231 | UFC 26: Ultimate Field Of Dreams-Tyrone Robert... | Decision |
34584 rows × 183 columns
fighter_buyrate = pd.read_sql('SELECT * FROM fighter_buyrate', connection)
# Sorting by Buyrate from highest to lowest
fighter_buyrate = fighter_buyrate.sort_values(by='Buyrate', ascending=False).reset_index(drop=True)
fighter_buyrate
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | Buyrate | Avg_Buyrate_Per_Event | PPV_Fights | Buyrate_PERCENTILE | PPV_Fights_PERCENTILE | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Conor McGregor | Southpaw | Yes | Yes | Yes | Yes | 13555166.0 | 1.355517e+06 | 10.0 | 100.000000 | 94.283647 |
| 1 | Georges St-Pierre | Orthodox | Yes | Yes | Yes | Yes | 12430000.0 | 5.650000e+05 | 22.0 | 99.956841 | 99.927641 |
| 2 | Jim Miller | Southpaw | No | No | No | No | 11741000.0 | 6.522778e+05 | 18.0 | 99.913681 | 99.493488 |
| 3 | Jon Jones | Orthodox | Yes | Yes | Yes | Yes | 10992000.0 | 6.465882e+05 | 17.0 | 99.870522 | 99.204052 |
| 4 | Demian Maia | Southpaw | No | No | Yes | No | 10907000.0 | 5.453500e+05 | 20.0 | 99.827363 | 99.710564 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2312 | Roque Martinez | Orthodox | No | No | No | No | 0.0 | NaN | NaN | 20.198533 | NaN |
| 2313 | Lara Procopio | Orthodox | No | No | No | No | 0.0 | NaN | NaN | 20.198533 | NaN |
| 2314 | Justin Jaynes | Orthodox | No | No | No | No | 0.0 | NaN | NaN | 20.198533 | NaN |
| 2315 | Montserrat Conejo | Southpaw | No | No | No | No | 0.0 | NaN | NaN | 20.198533 | NaN |
| 2316 | Tyrone Roberts | Orthodox | No | No | No | No | 0.0 | NaN | NaN | 20.198533 | NaN |
2317 rows × 11 columns
fb = fighter_buyrate[fighter_buyrate['Buyrate'] > 0]
# fb = fighter_buyrate[fighter_buyrate['PPV_Fights'] > 1]
# fb = fb.dropna(subset=['Buyrate'])
# fb = fighter_buyrate
# Calculate the counts for the top 2%, 5%, and 10% of the dataset
top_1_percent_count = int(len(fb) * 0.01)
top_10_percent_count = int(len(fb) * 0.1)
# Sort the dataset by 'Buyrate' descending
fb_sorted_by_buyrate = fb.sort_values(by='Buyrate', ascending=False)
# Extract the top 1% and 10% earners
top_1_percent_earners = fb_sorted_by_buyrate.head(top_1_percent_count)
top_10_percent_earners = fb_sorted_by_buyrate.head(top_10_percent_count)
# Convert to lists for display
top_1_percent_list = top_1_percent_earners['FIGHTER'].tolist()
top_10_percent_list = top_10_percent_earners['FIGHTER'].tolist()
top_1_percent_earners
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | Buyrate | Avg_Buyrate_Per_Event | PPV_Fights | Buyrate_PERCENTILE | PPV_Fights_PERCENTILE | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Conor McGregor | Southpaw | Yes | Yes | Yes | Yes | 13555166.0 | 1.355517e+06 | 10.0 | 100.000000 | 94.283647 |
| 1 | Georges St-Pierre | Orthodox | Yes | Yes | Yes | Yes | 12430000.0 | 5.650000e+05 | 22.0 | 99.956841 | 99.927641 |
| 2 | Jim Miller | Southpaw | No | No | No | No | 11741000.0 | 6.522778e+05 | 18.0 | 99.913681 | 99.493488 |
| 3 | Jon Jones | Orthodox | Yes | Yes | Yes | Yes | 10992000.0 | 6.465882e+05 | 17.0 | 99.870522 | 99.204052 |
| 4 | Demian Maia | Southpaw | No | No | Yes | No | 10907000.0 | 5.453500e+05 | 20.0 | 99.827363 | 99.710564 |
| 5 | Anderson Silva | Southpaw | Yes | No | Yes | Yes | 10739000.0 | 5.113810e+05 | 21.0 | 99.784204 | 99.782923 |
| 6 | Johny Hendricks | Southpaw | Yes | No | Yes | Yes | 9924000.0 | 6.616000e+05 | 15.0 | 99.741044 | 98.625181 |
| 7 | Frank Mir | Southpaw | Yes | Yes | Yes | Yes | 9519000.0 | 4.326818e+05 | 22.0 | 99.697885 | 99.927641 |
| 8 | Donald Cerrone | Orthodox | No | No | Yes | No | 9378429.0 | 6.252286e+05 | 15.0 | 99.654726 | 98.625181 |
| 9 | Diego Sanchez | Southpaw | No | No | Yes | No | 9126000.0 | 6.084000e+05 | 15.0 | 99.611567 | 98.625181 |
| 10 | Jeremy Stephens | Orthodox | No | No | No | No | 8625000.0 | 6.160714e+05 | 14.0 | 99.568407 | 98.263386 |
| 11 | Dustin Poirier | Southpaw | No | Yes | Yes | Yes | 8524737.0 | 5.683158e+05 | 15.0 | 99.525248 | 98.335745 |
| 12 | Jon Fitch | Orthodox | No | No | Yes | No | 8519000.0 | 6.085000e+05 | 14.0 | 99.482089 | 97.937771 |
top_10_percent_earners
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | Buyrate | Avg_Buyrate_Per_Event | PPV_Fights | Buyrate_PERCENTILE | PPV_Fights_PERCENTILE | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Conor McGregor | Southpaw | Yes | Yes | Yes | Yes | 13555166.0 | 1.355517e+06 | 10.0 | 100.000000 | 94.283647 |
| 1 | Georges St-Pierre | Orthodox | Yes | Yes | Yes | Yes | 12430000.0 | 5.650000e+05 | 22.0 | 99.956841 | 99.927641 |
| 2 | Jim Miller | Southpaw | No | No | No | No | 11741000.0 | 6.522778e+05 | 18.0 | 99.913681 | 99.493488 |
| 3 | Jon Jones | Orthodox | Yes | Yes | Yes | Yes | 10992000.0 | 6.465882e+05 | 17.0 | 99.870522 | 99.204052 |
| 4 | Demian Maia | Southpaw | No | No | Yes | No | 10907000.0 | 5.453500e+05 | 20.0 | 99.827363 | 99.710564 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 133 | Dominick Cruz | Orthodox | Yes | No | Yes | Yes | 3975000.0 | 5.678571e+05 | 7.0 | 94.259819 | 88.205499 |
| 134 | Krzysztof Soszynski | Switch | No | No | No | No | 3935000.0 | 5.621429e+05 | 7.0 | 94.216659 | 88.205499 |
| 135 | Max Griffin | Orthodox | No | No | No | No | 3930000.0 | 9.825000e+05 | 4.0 | 94.173500 | 74.963821 |
| 136 | Derrick Lewis | Orthodox | No | No | Yes | No | 3880000.0 | 7.760000e+05 | 5.0 | 94.130341 | 81.150507 |
| 137 | Aaron Riley | Southpaw | No | No | No | No | 3830000.0 | 6.383333e+05 | 6.0 | 94.087182 | 85.419682 |
138 rows × 11 columns
event_buyrate = pd.read_sql('SELECT * FROM event_buyrate', connection)
event_buyrate = event_buyrate[event_buyrate['Buyrate'] > 0]
event_buyrate
| EVENT | DATE | Buyrate | |
|---|---|---|---|
| 76 | UFC 274: Oliveira vs. Gaethje | 2022-05-07 | 400000.0 |
| 92 | UFC 269: Oliveira vs. Poirier | 2021-12-11 | 500000.0 |
| 96 | UFC 268: Usman vs. Covington 2 | 2021-11-06 | 700000.0 |
| 111 | UFC 264: Poirier vs. McGregor 3 | 2021-07-10 | 1800000.0 |
| 114 | UFC 263: Adesanya vs. Vettori 2 | 2021-06-12 | 600000.0 |
| ... | ... | ... | ... |
| 639 | UFC 37: High Impact | 2002-05-10 | 50000.0 |
| 640 | UFC 36: Worlds Collide | 2002-03-22 | 55000.0 |
| 641 | UFC 35: Throwdown | 2002-01-11 | 35000.0 |
| 642 | UFC 34: High Voltage | 2001-11-02 | 65000.0 |
| 643 | UFC 33: Victory in Vegas | 2001-09-28 | 75000.0 |
209 rows × 3 columns
frame = pd.read_sql('SELECT * FROM frame', connection)
frame
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | HEIGHT | WEIGHT | REACH | APE_INDEX | FRAME | HEIGHT_DIFF | REACH_DIFF | AGE_DIFF | AI_DIFF | FRAME_DIFF | OPP_HEIGHT | OPP_WEIGHT | OPP_REACH | OPP_APE_INDEX | OPP_FRAME | OPP_REACH_DIFF | OPP_AGE_DIFF | OPP_AI_DIFF | OPP_FRAME_DIFF | HEIGHT_DELTA | REACH_DELTA | AGE_DELTA | AI_DELTA | FRAME_DELTA | HEIGHT_DELTA_RANK | REACH_DELTA_RANK | AGE_DELTA_RANK | AI_DELTA_RANK | FRAME_DELTA_RANK | HEIGHT_DELTA_PERCENTILE | REACH_DELTA_PERCENTILE | FRAME_DELTA_PERCENTILE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Asu Almabayev | None | No | No | No | No | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 66.000000 | 135.000000 | 68.000000 | 1.030303 | 67.000000 | 1.124836 | 3.432091 | 0.010375 | 0.773653 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1 | CJ Vergara | Orthodox | No | No | No | No | 66.0 | 135.0 | 68.0 | 1.030303 | 67.0 | 0.422470 | 1.124836 | 2.432091 | 0.010375 | 0.773653 | 66.500000 | 125.000000 | 70.000000 | 1.052352 | 68.250000 | 3.124836 | -2.817909 | 0.032424 | 2.023653 | -0.500000 | -2.000000 | 4.750000 | -0.022049 | -1.250000 | 277.0 | 398.0 | 91.0 | 1404.0 | 444.0 | 42.521859 | 27.105384 | 30.556834 |
| 2 | Curtis Blaydes | Orthodox | No | No | No | No | 76.0 | 265.0 | 80.0 | 1.052632 | 78.0 | 1.138963 | 2.904203 | -3.661011 | 0.022708 | 2.021583 | 74.888889 | 245.777778 | 78.222222 | 1.044572 | 76.555556 | 1.126425 | 1.561212 | 0.014648 | 0.577138 | 1.111111 | 1.777778 | -5.222222 | 0.008060 | 1.444444 | 143.0 | 146.0 | 538.0 | 666.0 | 172.0 | 74.436263 | 79.682467 | 78.485964 |
| 3 | Jailton Almeida | Orthodox | No | No | No | No | 75.0 | 205.0 | 79.0 | 1.053333 | 77.0 | 1.066832 | 2.927534 | -0.532982 | 0.024388 | 1.997183 | 75.142857 | 239.571429 | 77.285714 | 1.028885 | 76.214286 | 1.213248 | 3.609875 | -0.000060 | 1.211469 | -0.142857 | 1.714286 | -4.142857 | 0.024448 | 0.785714 | 246.0 | 151.0 | 500.0 | 346.0 | 225.0 | 48.780488 | 79.061206 | 69.397147 |
| 4 | Benoit Saint Denis | Southpaw | No | No | No | No | 71.0 | 170.0 | 73.0 | 1.028169 | 72.0 | 1.203161 | 1.438362 | -2.626320 | 0.002739 | 1.320761 | 69.857143 | 157.857143 | 71.428571 | 1.022399 | 70.642857 | -0.133067 | 1.945109 | -0.003031 | -0.036382 | 1.142857 | 1.571429 | -4.571429 | 0.005770 | 1.357143 | 142.0 | 159.0 | 517.0 | 728.0 | 177.0 | 74.574321 | 77.542568 | 77.841694 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2312 | Joao Roque | Orthodox | No | No | No | No | 66.0 | 155.0 | 66.0 | 1.000000 | 66.0 | -3.541575 | -5.279980 | -1.101245 | -0.025164 | -4.410778 | 67.000000 | 145.000000 | 70.000000 | 1.044776 | 68.500000 | -1.279980 | -4.101245 | 0.019612 | -1.910778 | -1.000000 | -4.000000 | 3.000000 | -0.044776 | -2.500000 | 319.0 | 479.0 | 150.0 | 1792.0 | 532.0 | 32.512655 | 9.433962 | 14.450069 |
| 2313 | Marcelo Aguiar | Orthodox | No | No | No | No | 70.0 | 170.0 | 70.0 | 1.000000 | 70.0 | -1.328423 | -3.251590 | NaN | -0.027023 | -2.290007 | 69.000000 | 170.000000 | 73.000000 | 1.057971 | 71.000000 | -0.251590 | -3.776772 | 0.030948 | -1.290007 | 1.000000 | -3.000000 | NaN | -0.057971 | -1.000000 | 148.0 | 448.0 | NaN | 1868.0 | 416.0 | 71.514036 | 16.175794 | 35.687989 |
| 2314 | Adrian Serrano | None | No | No | No | No | 68.0 | 170.0 | 68.0 | 1.000000 | 68.0 | -3.328423 | -5.251590 | 6.223228 | -0.027023 | -4.290007 | 70.000000 | 170.000000 | 70.000000 | 1.000000 | 70.000000 | -3.251590 | -1.776772 | -0.027023 | -2.290007 | -2.000000 | -2.000000 | 8.000000 | 0.000000 | -2.000000 | 379.0 | 398.0 | 29.0 | 839.0 | 506.0 | 16.820064 | 27.105384 | 19.696272 |
| 2315 | David Dodd | Orthodox | No | No | No | No | 74.0 | 200.0 | 74.0 | 1.000000 | 74.0 | 1.331878 | -0.782579 | -4.365562 | -0.029232 | 0.274650 | 69.000000 | 185.000000 | 69.000000 | 1.000000 | 69.000000 | -5.782579 | NaN | -0.029232 | -4.725350 | 5.000000 | 5.000000 | NaN | 0.000000 | 5.000000 | 12.0 | 30.0 | NaN | 839.0 | 17.0 | 98.918546 | 97.468937 | 98.803497 |
| 2316 | Tyrone Roberts | Orthodox | No | No | No | No | 69.0 | 185.0 | 69.0 | 1.000000 | 69.0 | -3.668122 | -5.782579 | NaN | -0.029232 | -4.725350 | 74.000000 | 200.000000 | 74.000000 | 1.000000 | 74.000000 | -0.782579 | -4.365562 | -0.029232 | 0.274650 | -5.000000 | -5.000000 | NaN | 0.000000 | -5.000000 | 458.0 | 497.0 | NaN | 839.0 | 604.0 | 1.265532 | 4.970087 | 1.978831 |
2317 rows × 38 columns
career = pd.read_sql('SELECT * FROM career', connection)
career
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | KD | TD | SUB.ATT | REV | CTRL | HEAD | BODY | LEG | DISTANCE | CLINCH | GROUND | SIG | TOT.SIG | STR | TOTAL.STR | TOTAL.TD | TOTAL.HEAD | TOTAL.BODY | TOTAL.LEG | TOTAL.DISTANCE | TOTAL.CLINCH | TOTAL.GROUND | OPP_KD | OPP_TD | OPP_SUB.ATT | OPP_REV | OPP_CTRL | OPP_HEAD | OPP_BODY | OPP_LEG | OPP_DISTANCE | OPP_CLINCH | OPP_GROUND | OPP_SIG | OPP_TOT.SIG | OPP_STR | OPP_TOTAL.STR | OPP_TOTAL.TD | OPP_TOTAL.HEAD | OPP_TOTAL.BODY | OPP_TOTAL.LEG | OPP_TOTAL.DISTANCE | OPP_TOTAL.CLINCH | OPP_TOTAL.GROUND | TD_DEF | HEAD_DEF | BODY_DEF | LEG_DEF | DISTANCE_DEF | CLINCH_DEF | GROUND_DEF | SIG_DEF | STR_DEF | TD_% | HEAD_% | BODY_% | LEG_% | DISTANCE_% | CLINCH_% | GROUND_% | SIG_% | STR_% | TD_DEF_% | HEAD_DEF_% | BODY_DEF_% | LEG_DEF_% | DISTANCE_DEF_% | CLINCH_DEF_% | GROUND_DEF_% | SIG_DEF_% | STR_DEF_% | KD_PERCENTILE | TD_PERCENTILE | SUB.ATT_PERCENTILE | REV_PERCENTILE | CTRL_PERCENTILE | HEAD_PERCENTILE | BODY_PERCENTILE | LEG_PERCENTILE | DISTANCE_PERCENTILE | CLINCH_PERCENTILE | GROUND_PERCENTILE | SIG_PERCENTILE | STR_PERCENTILE | STR_%_PERCENTILE | TD_DEF_PERCENTILE | HEAD_DEF_PERCENTILE | BODY_DEF_PERCENTILE | LEG_DEF_PERCENTILE | DISTANCE_DEF_PERCENTILE | CLINCH_DEF_PERCENTILE | GROUND_DEF_PERCENTILE | SIG_DEF_PERCENTILE | STR_DEF_PERCENTILE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Asu Almabayev | None | No | No | No | No | 0.0 | 9.0 | 0.0 | 0.0 | 9.533333 | 22.0 | 9.0 | 13.0 | 23.0 | 8.0 | 13.0 | 44.0 | 77.0 | 85.0 | 132.0 | 14.0 | 53.0 | 11.0 | 13.0 | 51.0 | 10.0 | 16.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.233333 | 13.0 | 14.0 | 2.0 | 22.0 | 4.0 | 3.0 | 29.0 | 53.0 | 66.0 | 92.0 | 0.0 | 32.0 | 17.0 | 4.0 | 46.0 | 4.0 | 3.0 | 0.0 | 19.0 | 3.0 | 2.0 | 24.0 | 0.0 | 0.0 | 24.0 | 26.0 | 0.642857 | 0.415094 | 0.818182 | 1.000000 | 0.450980 | 0.800000 | 0.812500 | 0.571429 | 0.643939 | NaN | 0.593750 | 0.176471 | 0.500000 | 0.521739 | 0.000000 | 0.000000 | 0.452830 | 0.282609 | 26.888218 | 76.866638 | 21.255934 | 29.477773 | 57.833405 | 22.852827 | 26.866638 | 41.583945 | 21.709107 | 33.987915 | 49.482089 | 24.492879 | 27.492447 | 83.506045 | 7.466552 | 9.861890 | 23.327579 | 35.930082 | 15.235218 | 5.955978 | 6.689685 | 10.876133 | 10.142426 |
| 1 | CJ Vergara | Orthodox | No | No | No | No | 0.0 | 0.0 | 1.0 | 1.0 | 10.750000 | 203.0 | 149.0 | 52.0 | 285.0 | 35.0 | 84.0 | 404.0 | 700.0 | 579.0 | 895.0 | 0.0 | 448.0 | 193.0 | 59.0 | 531.0 | 46.0 | 123.0 | 2.0 | 16.0 | 5.0 | 2.0 | 20.200000 | 222.0 | 91.0 | 49.0 | 314.0 | 24.0 | 24.0 | 362.0 | 762.0 | 438.0 | 858.0 | 40.0 | 588.0 | 121.0 | 53.0 | 699.0 | 32.0 | 31.0 | 24.0 | 366.0 | 30.0 | 4.0 | 385.0 | 8.0 | 7.0 | 400.0 | 420.0 | NaN | 0.453125 | 0.772021 | 0.881356 | 0.536723 | 0.760870 | 0.682927 | 0.577143 | 0.646927 | 0.600000 | 0.622449 | 0.247934 | 0.075472 | 0.550787 | 0.250000 | 0.225806 | 0.524934 | 0.489510 | 26.888218 | 11.588261 | 51.143720 | 69.486405 | 60.595598 | 76.391886 | 93.029780 | 77.427708 | 82.088908 | 70.716444 | 89.512300 | 82.239965 | 81.635736 | 83.894646 | 85.304273 | 76.931377 | 76.413466 | 48.640483 | 78.161416 | 50.107898 | 45.986189 | 76.413466 | 76.132931 |
| 2 | Curtis Blaydes | Orthodox | No | No | No | No | 2.0 | 62.0 | 0.0 | 0.0 | 80.250000 | 395.0 | 69.0 | 110.0 | 260.0 | 73.0 | 241.0 | 574.0 | 1144.0 | 1016.0 | 1681.0 | 116.0 | 941.0 | 80.0 | 123.0 | 754.0 | 91.0 | 299.0 | 4.0 | 13.0 | 2.0 | 0.0 | 7.650000 | 224.0 | 47.0 | 27.0 | 242.0 | 41.0 | 15.0 | 298.0 | 739.0 | 530.0 | 985.0 | 19.0 | 640.0 | 67.0 | 32.0 | 668.0 | 52.0 | 19.0 | 6.0 | 416.0 | 20.0 | 5.0 | 426.0 | 11.0 | 4.0 | 441.0 | 455.0 | 0.534483 | 0.419766 | 0.862500 | 0.894309 | 0.344828 | 0.802198 | 0.806020 | 0.501748 | 0.604402 | 0.315789 | 0.650000 | 0.298507 | 0.156250 | 0.637725 | 0.211538 | 0.210526 | 0.596752 | 0.461929 | 76.542943 | 99.525248 | 21.255934 | 29.477773 | 98.575744 | 90.504963 | 77.924040 | 91.735002 | 79.909366 | 87.915408 | 99.050496 | 89.490721 | 92.706085 | 74.265976 | 50.841606 | 79.844627 | 64.911524 | 53.582218 | 80.146741 | 59.710833 | 33.448425 | 78.700906 | 78.485110 |
| 3 | Jailton Almeida | Orthodox | No | No | No | No | 0.0 | 25.0 | 8.0 | 1.0 | 47.950000 | 143.0 | 8.0 | 2.0 | 8.0 | 3.0 | 142.0 | 153.0 | 232.0 | 328.0 | 442.0 | 42.0 | 216.0 | 10.0 | 6.0 | 22.0 | 8.0 | 202.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.100000 | 34.0 | 3.0 | 1.0 | 4.0 | 1.0 | 33.0 | 38.0 | 68.0 | 74.0 | 115.0 | 2.0 | 61.0 | 6.0 | 1.0 | 19.0 | 1.0 | 48.0 | 2.0 | 27.0 | 3.0 | 0.0 | 15.0 | 0.0 | 15.0 | 30.0 | 41.0 | 0.595238 | 0.662037 | 0.800000 | 0.333333 | 0.363636 | 0.375000 | 0.702970 | 0.659483 | 0.742081 | 1.000000 | 0.442623 | 0.500000 | 0.000000 | 0.789474 | 0.000000 | 0.312500 | 0.441176 | 0.356522 | 26.888218 | 94.238239 | 91.562365 | 69.486405 | 94.216659 | 65.947346 | 24.622356 | 14.436772 | 11.631420 | 18.644799 | 96.266724 | 55.761761 | 64.954683 | 95.293610 | 28.226155 | 13.508848 | 23.327579 | 9.387139 | 11.242987 | 5.955978 | 68.558481 | 13.487268 | 15.537333 |
| 4 | Benoit Saint Denis | Southpaw | No | No | No | No | 4.0 | 16.0 | 5.0 | 1.0 | 24.416667 | 159.0 | 85.0 | 43.0 | 166.0 | 50.0 | 71.0 | 287.0 | 525.0 | 439.0 | 700.0 | 43.0 | 372.0 | 108.0 | 45.0 | 335.0 | 64.0 | 126.0 | 1.0 | 4.0 | 6.0 | 0.0 | 3.666667 | 159.0 | 67.0 | 25.0 | 193.0 | 50.0 | 8.0 | 251.0 | 439.0 | 299.0 | 497.0 | 13.0 | 336.0 | 76.0 | 27.0 | 360.0 | 71.0 | 8.0 | 9.0 | 177.0 | 9.0 | 2.0 | 167.0 | 21.0 | 0.0 | 188.0 | 198.0 | 0.372093 | 0.427419 | 0.787037 | 0.955556 | 0.495522 | 0.781250 | 0.563492 | 0.546667 | 0.627143 | 0.692308 | 0.526786 | 0.118421 | 0.074074 | 0.463889 | 0.295775 | 0.000000 | 0.428246 | 0.398390 | 89.404402 | 88.044886 | 83.707380 | 69.486405 | 81.614156 | 69.249029 | 82.585240 | 72.766508 | 67.695296 | 79.499353 | 86.944325 | 72.701770 | 73.176521 | 80.440415 | 61.609840 | 54.963315 | 44.993526 | 35.930082 | 54.294346 | 77.470868 | 6.689685 | 53.970652 | 53.172205 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2312 | Joao Roque | Orthodox | No | No | No | No | 0.0 | 1.0 | 0.0 | 0.0 | 0.266667 | 2.0 | 2.0 | 1.0 | 5.0 | 0.0 | 0.0 | 5.0 | 59.0 | 5.0 | 59.0 | 18.0 | 43.0 | 6.0 | 10.0 | 57.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 2.216667 | 4.0 | 0.0 | 0.0 | 3.0 | 1.0 | 0.0 | 4.0 | 84.0 | 18.0 | 98.0 | 0.0 | 84.0 | 0.0 | 0.0 | 77.0 | 1.0 | 6.0 | 0.0 | 80.0 | 0.0 | 0.0 | 74.0 | 0.0 | 6.0 | 80.0 | 80.0 | 0.055556 | 0.046512 | 0.333333 | 0.100000 | 0.087719 | 0.000000 | 0.000000 | 0.084746 | 0.084746 | NaN | 0.952381 | NaN | NaN | 0.961039 | 0.000000 | 1.000000 | 0.952381 | 0.816327 | 26.888218 | 29.736729 | 21.255934 | 29.477773 | 9.667674 | 5.049633 | 10.703496 | 9.451877 | 8.782909 | 4.294346 | 8.610272 | 5.114372 | 3.064307 | 0.604491 | 7.466552 | 33.275788 | 4.661200 | 9.387139 | 33.664221 | 5.955978 | 41.929219 | 30.621493 | 27.880880 |
| 2313 | Marcelo Aguiar | Orthodox | No | No | No | No | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 2.0 | 2.0 | 21.0 | 21.0 | 0.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 0.0 | 2.0 | 0.0 | 0.0 | 4.133333 | 14.0 | 4.0 | 0.0 | 0.0 | 0.0 | 18.0 | 18.0 | 31.0 | 29.0 | 42.0 | 3.0 | 27.0 | 4.0 | 0.0 | 2.0 | 0.0 | 29.0 | 1.0 | 13.0 | 0.0 | 0.0 | 2.0 | 0.0 | 11.0 | 13.0 | 13.0 | NaN | 1.000000 | NaN | NaN | NaN | NaN | 1.000000 | 1.000000 | 1.000000 | 0.333333 | 0.481481 | 0.000000 | NaN | 1.000000 | NaN | 0.379310 | 0.419355 | 0.309524 | 26.888218 | 11.588261 | 21.255934 | 29.477773 | 2.546396 | 5.049633 | 2.740613 | 3.366422 | 1.143720 | 4.294346 | 25.097108 | 2.675874 | 8.718170 | 99.827288 | 19.615883 | 7.034959 | 4.661200 | 9.387139 | 2.481657 | 5.955978 | 58.761329 | 6.258092 | 5.157531 |
| 2314 | Adrian Serrano | None | No | No | No | No | 0.0 | 0.0 | 0.0 | 1.0 | 3.116667 | 2.0 | 4.0 | 0.0 | 3.0 | 3.0 | 0.0 | 6.0 | 23.0 | 55.0 | 79.0 | 2.0 | 16.0 | 7.0 | 0.0 | 15.0 | 8.0 | 0.0 | 0.0 | 3.0 | 0.0 | 0.0 | 2.000000 | 15.0 | 5.0 | 6.0 | 21.0 | 3.0 | 2.0 | 26.0 | 55.0 | 51.0 | 85.0 | 6.0 | 41.0 | 7.0 | 7.0 | 44.0 | 5.0 | 6.0 | 3.0 | 26.0 | 2.0 | 1.0 | 23.0 | 2.0 | 4.0 | 29.0 | 34.0 | 0.000000 | 0.125000 | 0.571429 | NaN | 0.200000 | 0.375000 | NaN | 0.260870 | 0.696203 | 0.500000 | 0.634146 | 0.285714 | 0.142857 | 0.522727 | 0.400000 | 0.666667 | 0.527273 | 0.400000 | 26.888218 | 11.588261 | 21.255934 | 69.486405 | 31.160984 | 5.049633 | 15.645231 | 3.366422 | 6.560207 | 18.644799 | 8.610272 | 5.848079 | 19.443246 | 90.932642 | 35.261114 | 12.947777 | 18.277946 | 25.377644 | 14.889944 | 22.442814 | 33.448425 | 12.990937 | 13.163574 |
| 2315 | David Dodd | Orthodox | No | No | No | No | 0.0 | 0.0 | 0.0 | 0.0 | 0.033333 | 5.0 | 5.0 | 0.0 | 3.0 | 5.0 | 2.0 | 10.0 | 31.0 | 27.0 | 49.0 | 2.0 | 25.0 | 6.0 | 0.0 | 22.0 | 6.0 | 3.0 | 0.0 | 1.0 | 0.0 | 0.0 | 4.783333 | 11.0 | 3.0 | 2.0 | 12.0 | 4.0 | 0.0 | 16.0 | 60.0 | 105.0 | 163.0 | 1.0 | 53.0 | 5.0 | 2.0 | 45.0 | 13.0 | 2.0 | 0.0 | 42.0 | 2.0 | 0.0 | 33.0 | 9.0 | 2.0 | 44.0 | 58.0 | 0.000000 | 0.200000 | 0.833333 | NaN | 0.136364 | 0.833333 | 0.666667 | 0.322581 | 0.551020 | 0.000000 | 0.792453 | 0.400000 | 0.000000 | 0.733333 | 0.692308 | 1.000000 | 0.733333 | 0.355828 | 26.888218 | 11.588261 | 21.255934 | 29.477773 | 5.761761 | 8.610272 | 17.716875 | 3.366422 | 6.560207 | 25.312905 | 25.097108 | 8.394476 | 10.681916 | 56.563040 | 7.466552 | 19.594303 | 18.277946 | 9.387139 | 18.536901 | 53.323263 | 22.874407 | 18.536901 | 20.759603 |
| 2316 | Tyrone Roberts | Orthodox | No | No | No | No | 0.0 | 1.0 | 0.0 | 0.0 | 4.783333 | 11.0 | 3.0 | 2.0 | 12.0 | 4.0 | 0.0 | 16.0 | 60.0 | 105.0 | 163.0 | 1.0 | 53.0 | 5.0 | 2.0 | 45.0 | 13.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.033333 | 5.0 | 5.0 | 0.0 | 3.0 | 5.0 | 2.0 | 10.0 | 31.0 | 27.0 | 49.0 | 2.0 | 25.0 | 6.0 | 0.0 | 22.0 | 6.0 | 3.0 | 2.0 | 20.0 | 1.0 | 0.0 | 19.0 | 1.0 | 1.0 | 21.0 | 22.0 | 1.000000 | 0.207547 | 0.600000 | 1.000000 | 0.266667 | 0.307692 | 0.000000 | 0.266667 | 0.644172 | 1.000000 | 0.800000 | 0.166667 | NaN | 0.863636 | 0.166667 | 0.333333 | 0.677419 | 0.448980 | 26.888218 | 29.736729 | 21.255934 | 29.477773 | 39.879154 | 13.875701 | 13.465688 | 14.436772 | 14.954683 | 22.183858 | 8.610272 | 11.631420 | 32.801036 | 83.549223 | 28.226155 | 10.336642 | 12.473025 | 9.387139 | 12.947777 | 15.580492 | 16.508416 | 9.883470 | 8.696590 |
2317 rows × 100 columns
fight_average = pd.read_sql('SELECT * FROM fight_average', connection)
fight_average
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | FA_FIGHTER | FA_KD | FA_TD | FA_SUB.ATT | FA_REV | FA_CTRL | FA_HEAD | FA_BODY | FA_LEG | FA_DISTANCE | FA_CLINCH | FA_GROUND | FA_SIG | FA_TOT.SIG | FA_STR | FA_TOTAL.STR | FA_TOTAL.TD | FA_TOTAL.HEAD | FA_TOTAL.BODY | FA_TOTAL.LEG | FA_TOTAL.DISTANCE | FA_TOTAL.CLINCH | FA_TOTAL.GROUND | FA_OPP_KD | FA_OPP_TD | FA_OPP_SUB.ATT | FA_OPP_REV | FA_OPP_CTRL | FA_OPP_HEAD | FA_OPP_BODY | FA_OPP_LEG | FA_OPP_DISTANCE | FA_OPP_CLINCH | FA_OPP_GROUND | FA_OPP_SIG | FA_OPP_TOT.SIG | FA_OPP_STR | FA_OPP_TOTAL.STR | FA_OPP_TOTAL.TD | FA_OPP_TOTAL.HEAD | FA_OPP_TOTAL.BODY | FA_OPP_TOTAL.LEG | FA_OPP_TOTAL.DISTANCE | FA_OPP_TOTAL.CLINCH | FA_OPP_TOTAL.GROUND | FA_TD_DEF | FA_HEAD_DEF | FA_BODY_DEF | FA_LEG_DEF | FA_DISTANCE_DEF | FA_CLINCH_DEF | FA_GROUND_DEF | FA_SIG_DEF | FA_STR_DEF | FA_TD_% | FA_HEAD_% | FA_BODY_% | FA_LEG_% | FA_DISTANCE_% | FA_CLINCH_% | FA_GROUND_% | FA_SIG_% | FA_STR_% | FA_TD_DEF_% | FA_HEAD_DEF_% | FA_BODY_DEF_% | FA_LEG_DEF_% | FA_DISTANCE_DEF_% | FA_CLINCH_DEF_% | FA_GROUND_DEF_% | FA_SIG_DEF_% | FA_STR_DEF_% | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Asu Almabayev | None | No | No | No | No | Asu Almabayev | 0.000000 | 3.000000 | 0.000000 | 0.000000 | 3.177778 | 7.333333 | 3.000000 | 4.333333 | 7.666667 | 2.666667 | 4.333333 | 14.666667 | 25.666667 | 28.333333 | 44.000000 | 4.666667 | 17.666667 | 3.666667 | 4.333333 | 17.000000 | 3.333333 | 5.333333 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.077778 | 4.333333 | 4.666667 | 0.666667 | 7.333333 | 1.333333 | 1.000000 | 9.666667 | 17.666667 | 22.000000 | 30.666667 | 0.000000 | 10.666667 | 5.666667 | 1.333333 | 15.333333 | 1.333333 | 1.000000 | 0.000000 | 6.333333 | 1.000000 | 0.666667 | 8.000000 | 0.000000 | 0.000000 | 8.000000 | 8.666667 | 0.642857 | 0.415094 | 0.818182 | 1.000000 | 0.450980 | 0.800000 | 0.812500 | 0.571429 | 0.643939 | NaN | 0.593750 | 0.176471 | 0.500000 | 0.521739 | 0.000000 | 0.000000 | 0.452830 | 0.282609 |
| 1 | CJ Vergara | Orthodox | No | No | No | No | CJ Vergara | 0.000000 | 0.000000 | 0.083333 | 0.083333 | 0.694444 | 13.222222 | 8.861111 | 3.194444 | 17.111111 | 2.027778 | 6.138889 | 25.277778 | 43.722222 | 35.694444 | 55.388889 | 0.000000 | 28.388889 | 11.694444 | 3.638889 | 31.944444 | 2.694444 | 9.083333 | 0.166667 | 1.000000 | 0.388889 | 0.111111 | 1.316204 | 13.611111 | 5.500000 | 2.944444 | 19.027778 | 1.527778 | 1.500000 | 22.055556 | 46.583333 | 26.611111 | 52.277778 | 2.500000 | 36.055556 | 7.333333 | 3.194444 | 42.500000 | 2.083333 | 2.000000 | 1.500000 | 22.444444 | 1.833333 | 0.250000 | 23.472222 | 0.555556 | 0.500000 | 24.527778 | 25.666667 | NaN | 0.465753 | 0.757720 | 0.877863 | 0.535652 | 0.752577 | 0.675841 | 0.578145 | 0.644433 | 0.600000 | 0.622496 | 0.250000 | 0.078261 | 0.552288 | 0.266667 | 0.250000 | 0.526535 | 0.490967 |
| 2 | Curtis Blaydes | Orthodox | No | No | No | No | Curtis Blaydes | 0.055556 | 1.266667 | 0.000000 | 0.000000 | 1.519198 | 9.433333 | 1.475926 | 2.385185 | 6.462963 | 1.612963 | 5.218519 | 13.294444 | 27.970370 | 22.492593 | 39.153704 | 2.444444 | 23.518519 | 1.692593 | 2.759259 | 19.392593 | 2.029630 | 6.548148 | 0.157407 | 0.344444 | 0.037037 | 0.000000 | 0.201883 | 6.401852 | 1.224074 | 0.727778 | 6.603704 | 1.101852 | 0.648148 | 8.353704 | 19.874074 | 12.364815 | 24.161111 | 0.485185 | 17.364815 | 1.670370 | 0.838889 | 17.751852 | 1.344444 | 0.777778 | 0.140741 | 10.962963 | 0.446296 | 0.111111 | 11.148148 | 0.242593 | 0.129630 | 11.520370 | 11.796296 | 0.518182 | 0.401102 | 0.871991 | 0.864430 | 0.333270 | 0.794708 | 0.796946 | 0.475305 | 0.574469 | 0.290076 | 0.631332 | 0.267184 | 0.132450 | 0.627999 | 0.180441 | 0.166667 | 0.579668 | 0.488235 |
| 3 | Jailton Almeida | Orthodox | No | No | No | No | Jailton Almeida | 0.000000 | 2.028571 | 0.685714 | 0.028571 | 3.565476 | 13.557143 | 0.571429 | 0.100000 | 0.428571 | 0.085714 | 13.714286 | 14.228571 | 22.457143 | 26.142857 | 37.014286 | 3.000000 | 21.228571 | 0.857143 | 0.371429 | 1.771429 | 0.228571 | 20.457143 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.037857 | 1.700000 | 0.085714 | 0.142857 | 0.271429 | 0.028571 | 1.628571 | 1.928571 | 3.400000 | 5.800000 | 8.042857 | 0.057143 | 3.085714 | 0.171429 | 0.142857 | 1.142857 | 0.028571 | 2.228571 | 0.057143 | 1.385714 | 0.085714 | 0.000000 | 0.871429 | 0.000000 | 0.600000 | 1.471429 | 2.242857 | 0.676190 | 0.638627 | 0.666667 | 0.269231 | 0.241935 | 0.375000 | 0.670391 | 0.633588 | 0.706291 | 1.000000 | 0.449074 | 0.500000 | 0.000000 | 0.762500 | 0.000000 | 0.269231 | 0.432773 | 0.278863 |
| 4 | Benoit Saint Denis | Southpaw | No | No | No | No | Benoit Saint Denis | 0.357143 | 1.238095 | 0.500000 | 0.142857 | 1.931746 | 10.833333 | 6.380952 | 3.047619 | 11.404762 | 3.642857 | 5.214286 | 20.261905 | 37.119048 | 32.666667 | 51.357143 | 3.238095 | 25.642857 | 8.285714 | 3.190476 | 23.309524 | 4.690476 | 9.119048 | 0.071429 | 0.428571 | 0.428571 | 0.000000 | 0.255952 | 9.857143 | 4.261905 | 1.619048 | 11.976190 | 3.238095 | 0.523810 | 15.738095 | 27.547619 | 18.952381 | 31.571429 | 0.976190 | 21.023810 | 4.785714 | 1.738095 | 22.642857 | 4.380952 | 0.523810 | 0.547619 | 11.166667 | 0.523810 | 0.119048 | 10.666667 | 1.142857 | 0.000000 | 11.809524 | 12.619048 | 0.382353 | 0.422470 | 0.770115 | 0.955224 | 0.489275 | 0.776650 | 0.571802 | 0.545863 | 0.636069 | 0.560976 | 0.531144 | 0.109453 | 0.068493 | 0.471083 | 0.260870 | 0.000000 | 0.428695 | 0.399698 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2312 | Joao Roque | Orthodox | No | No | No | No | Joao Roque | 0.000000 | 0.333333 | 0.000000 | 0.000000 | 0.088889 | 0.666667 | 0.666667 | 0.333333 | 1.666667 | 0.000000 | 0.000000 | 1.666667 | 19.666667 | 1.666667 | 19.666667 | 6.000000 | 14.333333 | 2.000000 | 3.333333 | 19.000000 | 0.333333 | 0.333333 | 0.000000 | 0.000000 | 0.000000 | 0.333333 | 0.738889 | 1.333333 | 0.000000 | 0.000000 | 1.000000 | 0.333333 | 0.000000 | 1.333333 | 28.000000 | 6.000000 | 32.666667 | 0.000000 | 28.000000 | 0.000000 | 0.000000 | 25.666667 | 0.333333 | 2.000000 | 0.000000 | 26.666667 | 0.000000 | 0.000000 | 24.666667 | 0.000000 | 2.000000 | 26.666667 | 26.666667 | 0.055556 | 0.046512 | 0.333333 | 0.100000 | 0.087719 | 0.000000 | 0.000000 | 0.084746 | 0.084746 | NaN | 0.952381 | NaN | NaN | 0.961039 | 0.000000 | 1.000000 | 0.952381 | 0.816327 |
| 2313 | Marcelo Aguiar | Orthodox | No | No | No | No | Marcelo Aguiar | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 2.000000 | 2.000000 | 2.000000 | 21.000000 | 21.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 2.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | 4.133333 | 14.000000 | 4.000000 | 0.000000 | 0.000000 | 0.000000 | 18.000000 | 18.000000 | 31.000000 | 29.000000 | 42.000000 | 3.000000 | 27.000000 | 4.000000 | 0.000000 | 2.000000 | 0.000000 | 29.000000 | 1.000000 | 13.000000 | 0.000000 | 0.000000 | 2.000000 | 0.000000 | 11.000000 | 13.000000 | 13.000000 | NaN | 1.000000 | NaN | NaN | NaN | NaN | 1.000000 | 1.000000 | 1.000000 | 0.333333 | 0.481481 | 0.000000 | NaN | 1.000000 | NaN | 0.379310 | 0.419355 | 0.309524 |
| 2314 | Adrian Serrano | None | No | No | No | No | Adrian Serrano | 0.000000 | 0.000000 | 0.000000 | 0.500000 | 1.558333 | 1.000000 | 2.000000 | 0.000000 | 1.500000 | 1.500000 | 0.000000 | 3.000000 | 11.500000 | 27.500000 | 39.500000 | 1.000000 | 8.000000 | 3.500000 | 0.000000 | 7.500000 | 4.000000 | 0.000000 | 0.000000 | 1.500000 | 0.000000 | 0.000000 | 1.000000 | 7.500000 | 2.500000 | 3.000000 | 10.500000 | 1.500000 | 1.000000 | 13.000000 | 27.500000 | 25.500000 | 42.500000 | 3.000000 | 20.500000 | 3.500000 | 3.500000 | 22.000000 | 2.500000 | 3.000000 | 1.500000 | 13.000000 | 1.000000 | 0.500000 | 11.500000 | 1.000000 | 2.000000 | 14.500000 | 17.000000 | 0.000000 | 0.125000 | 0.571429 | NaN | 0.200000 | 0.375000 | NaN | 0.260870 | 0.696203 | 0.500000 | 0.634146 | 0.285714 | 0.142857 | 0.522727 | 0.400000 | 0.666667 | 0.527273 | 0.400000 |
| 2315 | David Dodd | Orthodox | No | No | No | No | David Dodd | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.011111 | 1.666667 | 1.666667 | 0.000000 | 1.000000 | 1.666667 | 0.666667 | 3.333333 | 10.333333 | 9.000000 | 16.333333 | 0.666667 | 8.333333 | 2.000000 | 0.000000 | 7.333333 | 2.000000 | 1.000000 | 0.000000 | 0.333333 | 0.000000 | 0.000000 | 1.594444 | 3.666667 | 1.000000 | 0.666667 | 4.000000 | 1.333333 | 0.000000 | 5.333333 | 20.000000 | 35.000000 | 54.333333 | 0.333333 | 17.666667 | 1.666667 | 0.666667 | 15.000000 | 4.333333 | 0.666667 | 0.000000 | 14.000000 | 0.666667 | 0.000000 | 11.000000 | 3.000000 | 0.666667 | 14.666667 | 19.333333 | 0.000000 | 0.200000 | 0.833333 | NaN | 0.136364 | 0.833333 | 0.666667 | 0.322581 | 0.551020 | 0.000000 | 0.792453 | 0.400000 | 0.000000 | 0.733333 | 0.692308 | 1.000000 | 0.733333 | 0.355828 |
| 2316 | Tyrone Roberts | Orthodox | No | No | No | No | Tyrone Roberts | 0.000000 | 0.333333 | 0.000000 | 0.000000 | 1.594444 | 3.666667 | 1.000000 | 0.666667 | 4.000000 | 1.333333 | 0.000000 | 5.333333 | 20.000000 | 35.000000 | 54.333333 | 0.333333 | 17.666667 | 1.666667 | 0.666667 | 15.000000 | 4.333333 | 0.666667 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.011111 | 1.666667 | 1.666667 | 0.000000 | 1.000000 | 1.666667 | 0.666667 | 3.333333 | 10.333333 | 9.000000 | 16.333333 | 0.666667 | 8.333333 | 2.000000 | 0.000000 | 7.333333 | 2.000000 | 1.000000 | 0.666667 | 6.666667 | 0.333333 | 0.000000 | 6.333333 | 0.333333 | 0.333333 | 7.000000 | 7.333333 | 1.000000 | 0.207547 | 0.600000 | 1.000000 | 0.266667 | 0.307692 | 0.000000 | 0.266667 | 0.644172 | 1.000000 | 0.800000 | 0.166667 | NaN | 0.863636 | 0.166667 | 0.333333 | 0.677419 | 0.448980 |
2317 rows × 78 columns
round_average = pd.read_sql('SELECT * FROM round_average', connection)
round_average
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | RA_FIGHTER | RA_KD | RA_TD | RA_SUB.ATT | RA_REV | RA_CTRL | RA_HEAD | RA_BODY | RA_LEG | RA_DISTANCE | RA_CLINCH | RA_GROUND | RA_SIG | RA_TOT.SIG | RA_STR | RA_TOTAL.STR | RA_TOTAL.TD | RA_TOTAL.HEAD | RA_TOTAL.BODY | RA_TOTAL.LEG | RA_TOTAL.DISTANCE | RA_TOTAL.CLINCH | RA_TOTAL.GROUND | RA_OPP_KD | RA_OPP_TD | RA_OPP_SUB.ATT | RA_OPP_REV | RA_OPP_CTRL | RA_OPP_HEAD | RA_OPP_BODY | RA_OPP_LEG | RA_OPP_DISTANCE | RA_OPP_CLINCH | RA_OPP_GROUND | RA_OPP_SIG | RA_OPP_TOT.SIG | RA_OPP_STR | RA_OPP_TOTAL.STR | RA_OPP_TOTAL.TD | RA_OPP_TOTAL.HEAD | RA_OPP_TOTAL.BODY | RA_OPP_TOTAL.LEG | RA_OPP_TOTAL.DISTANCE | RA_OPP_TOTAL.CLINCH | RA_OPP_TOTAL.GROUND | RA_TD_DEF | RA_HEAD_DEF | RA_BODY_DEF | RA_LEG_DEF | RA_DISTANCE_DEF | RA_CLINCH_DEF | RA_GROUND_DEF | RA_SIG_DEF | RA_STR_DEF | RA_TD_% | RA_HEAD_% | RA_BODY_% | RA_LEG_% | RA_DISTANCE_% | RA_CLINCH_% | RA_GROUND_% | RA_SIG_% | RA_STR_% | RA_TD_DEF_% | RA_HEAD_DEF_% | RA_BODY_DEF_% | RA_LEG_DEF_% | RA_DISTANCE_DEF_% | RA_CLINCH_DEF_% | RA_GROUND_DEF_% | RA_SIG_DEF_% | RA_STR_DEF_% | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Asu Almabayev | None | No | No | No | No | Asu Almabayev | 0.000000 | 3.000000 | 0.000000 | 0.000000 | 3.177778 | 7.333333 | 3.000000 | 4.333333 | 7.666667 | 2.666667 | 4.333333 | 14.666667 | 25.666667 | 28.333333 | 44.000000 | 4.666667 | 17.666667 | 3.666667 | 4.333333 | 17.000000 | 3.333333 | 5.333333 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.077778 | 4.333333 | 4.666667 | 0.666667 | 7.333333 | 1.333333 | 1.000000 | 9.666667 | 17.666667 | 22.000000 | 30.666667 | 0.000000 | 10.666667 | 5.666667 | 1.333333 | 15.333333 | 1.333333 | 1.000000 | 0.000000 | 6.333333 | 1.000000 | 0.666667 | 8.000000 | 0.000000 | 0.000000 | 8.000000 | 8.666667 | 0.642857 | 0.415094 | 0.818182 | 1.000000 | 0.450980 | 0.800000 | 0.812500 | 0.571429 | 0.643939 | NaN | 0.593750 | 0.176471 | 0.500000 | 0.521739 | 0.000000 | 0.000000 | 0.452830 | 0.282609 |
| 1 | CJ Vergara | Orthodox | No | No | No | No | CJ Vergara | 0.000000 | 0.000000 | 0.055556 | 0.055556 | 0.681481 | 12.750000 | 9.861111 | 3.083333 | 18.250000 | 2.500000 | 4.944444 | 25.694444 | 44.611111 | 37.111111 | 57.305556 | 0.000000 | 28.472222 | 12.638889 | 3.500000 | 34.111111 | 3.250000 | 7.250000 | 0.111111 | 1.000000 | 0.305556 | 0.138889 | 1.258796 | 14.000000 | 5.777778 | 2.916667 | 19.722222 | 1.527778 | 1.444444 | 22.694444 | 48.111111 | 27.361111 | 54.027778 | 2.555556 | 37.333333 | 7.638889 | 3.138889 | 44.250000 | 2.027778 | 1.833333 | 1.555556 | 23.333333 | 1.861111 | 0.222222 | 24.527778 | 0.500000 | 0.388889 | 25.416667 | 26.666667 | NaN | 0.447805 | 0.780220 | 0.880952 | 0.535016 | 0.769231 | 0.681992 | 0.575965 | 0.647601 | 0.608696 | 0.625000 | 0.243636 | 0.070796 | 0.554300 | 0.246575 | 0.212121 | 0.528291 | 0.493573 |
| 2 | Curtis Blaydes | Orthodox | No | No | No | No | Curtis Blaydes | 0.024444 | 1.886667 | 0.000000 | 0.000000 | 2.670889 | 8.462222 | 1.548889 | 1.500000 | 5.433333 | 1.900000 | 4.177778 | 11.511111 | 21.655556 | 22.500000 | 35.106667 | 3.837778 | 18.253333 | 1.726667 | 1.675556 | 14.168889 | 2.197778 | 5.288889 | 0.046667 | 0.333333 | 0.026667 | 0.000000 | 0.216815 | 5.788889 | 1.411111 | 0.400000 | 6.882222 | 0.542222 | 0.175556 | 7.600000 | 19.677778 | 26.988889 | 40.008889 | 0.613333 | 17.313333 | 1.906667 | 0.457778 | 18.555556 | 0.902222 | 0.220000 | 0.280000 | 11.524444 | 0.495556 | 0.057778 | 11.673333 | 0.360000 | 0.044444 | 12.077778 | 13.020000 | 0.491604 | 0.463599 | 0.897040 | 0.895225 | 0.383469 | 0.864510 | 0.789916 | 0.531555 | 0.640904 | 0.456522 | 0.665640 | 0.259907 | 0.126214 | 0.629102 | 0.399015 | 0.202020 | 0.613778 | 0.325428 |
| 3 | Jailton Almeida | Orthodox | No | No | No | No | Jailton Almeida | 0.000000 | 1.476190 | 0.304762 | 0.200000 | 3.737937 | 8.580952 | 0.571429 | 0.266667 | 0.647619 | 0.600000 | 8.171429 | 9.419048 | 14.228571 | 27.542857 | 35.980952 | 3.409524 | 12.971429 | 0.628571 | 0.628571 | 1.409524 | 1.600000 | 11.219048 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.198730 | 3.676190 | 0.257143 | 0.028571 | 0.323810 | 0.200000 | 3.438095 | 3.961905 | 7.352381 | 5.923810 | 10.219048 | 0.400000 | 6.809524 | 0.514286 | 0.028571 | 1.933333 | 0.200000 | 5.219048 | 0.400000 | 3.133333 | 0.257143 | 0.000000 | 1.609524 | 0.000000 | 1.780952 | 3.390476 | 4.295238 | 0.432961 | 0.661527 | 0.909091 | 0.424242 | 0.459459 | 0.375000 | 0.728353 | 0.661981 | 0.765484 | 1.000000 | 0.460140 | 0.500000 | 0.000000 | 0.832512 | 0.000000 | 0.341241 | 0.461140 | 0.420317 |
| 4 | Benoit Saint Denis | Southpaw | No | No | No | No | Benoit Saint Denis | 0.209524 | 0.895238 | 0.257143 | 0.047619 | 1.433492 | 11.952381 | 6.561905 | 2.961905 | 14.514286 | 2.761905 | 4.200000 | 21.476190 | 39.304762 | 29.761905 | 49.047619 | 3.780952 | 28.190476 | 8.057143 | 3.057143 | 28.009524 | 3.504762 | 7.790476 | 0.066667 | 0.209524 | 0.323810 | 0.000000 | 0.547302 | 11.952381 | 6.828571 | 2.657143 | 16.561905 | 3.580952 | 1.295238 | 21.438095 | 38.009524 | 25.419048 | 42.790476 | 0.676190 | 27.085714 | 7.885714 | 3.038095 | 31.771429 | 4.942857 | 1.295238 | 0.466667 | 15.133333 | 1.057143 | 0.380952 | 15.209524 | 1.361905 | 0.000000 | 16.571429 | 17.371429 | 0.236776 | 0.423986 | 0.814421 | 0.968847 | 0.518191 | 0.788043 | 0.539120 | 0.546402 | 0.606796 | 0.690141 | 0.558720 | 0.134058 | 0.125392 | 0.478717 | 0.275530 | 0.000000 | 0.435981 | 0.405965 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2312 | Joao Roque | Orthodox | No | No | No | No | Joao Roque | 0.000000 | 0.333333 | 0.000000 | 0.000000 | 0.088889 | 0.666667 | 0.666667 | 0.333333 | 1.666667 | 0.000000 | 0.000000 | 1.666667 | 19.666667 | 1.666667 | 19.666667 | 6.000000 | 14.333333 | 2.000000 | 3.333333 | 19.000000 | 0.333333 | 0.333333 | 0.000000 | 0.000000 | 0.000000 | 0.333333 | 0.738889 | 1.333333 | 0.000000 | 0.000000 | 1.000000 | 0.333333 | 0.000000 | 1.333333 | 28.000000 | 6.000000 | 32.666667 | 0.000000 | 28.000000 | 0.000000 | 0.000000 | 25.666667 | 0.333333 | 2.000000 | 0.000000 | 26.666667 | 0.000000 | 0.000000 | 24.666667 | 0.000000 | 2.000000 | 26.666667 | 26.666667 | 0.055556 | 0.046512 | 0.333333 | 0.100000 | 0.087719 | 0.000000 | 0.000000 | 0.084746 | 0.084746 | NaN | 0.952381 | NaN | NaN | 0.961039 | 0.000000 | 1.000000 | 0.952381 | 0.816327 |
| 2313 | Marcelo Aguiar | Orthodox | No | No | No | No | Marcelo Aguiar | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 2.000000 | 2.000000 | 2.000000 | 21.000000 | 21.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 2.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | 4.133333 | 14.000000 | 4.000000 | 0.000000 | 0.000000 | 0.000000 | 18.000000 | 18.000000 | 31.000000 | 29.000000 | 42.000000 | 3.000000 | 27.000000 | 4.000000 | 0.000000 | 2.000000 | 0.000000 | 29.000000 | 1.000000 | 13.000000 | 0.000000 | 0.000000 | 2.000000 | 0.000000 | 11.000000 | 13.000000 | 13.000000 | NaN | 1.000000 | NaN | NaN | NaN | NaN | 1.000000 | 1.000000 | 1.000000 | 0.333333 | 0.481481 | 0.000000 | NaN | 1.000000 | NaN | 0.379310 | 0.419355 | 0.309524 |
| 2314 | Adrian Serrano | None | No | No | No | No | Adrian Serrano | 0.000000 | 0.000000 | 0.000000 | 0.500000 | 1.558333 | 1.000000 | 2.000000 | 0.000000 | 1.500000 | 1.500000 | 0.000000 | 3.000000 | 11.500000 | 27.500000 | 39.500000 | 1.000000 | 8.000000 | 3.500000 | 0.000000 | 7.500000 | 4.000000 | 0.000000 | 0.000000 | 1.500000 | 0.000000 | 0.000000 | 1.000000 | 7.500000 | 2.500000 | 3.000000 | 10.500000 | 1.500000 | 1.000000 | 13.000000 | 27.500000 | 25.500000 | 42.500000 | 3.000000 | 20.500000 | 3.500000 | 3.500000 | 22.000000 | 2.500000 | 3.000000 | 1.500000 | 13.000000 | 1.000000 | 0.500000 | 11.500000 | 1.000000 | 2.000000 | 14.500000 | 17.000000 | 0.000000 | 0.125000 | 0.571429 | NaN | 0.200000 | 0.375000 | NaN | 0.260870 | 0.696203 | 0.500000 | 0.634146 | 0.285714 | 0.142857 | 0.522727 | 0.400000 | 0.666667 | 0.527273 | 0.400000 |
| 2315 | David Dodd | Orthodox | No | No | No | No | David Dodd | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.011111 | 1.666667 | 1.666667 | 0.000000 | 1.000000 | 1.666667 | 0.666667 | 3.333333 | 10.333333 | 9.000000 | 16.333333 | 0.666667 | 8.333333 | 2.000000 | 0.000000 | 7.333333 | 2.000000 | 1.000000 | 0.000000 | 0.333333 | 0.000000 | 0.000000 | 1.594444 | 3.666667 | 1.000000 | 0.666667 | 4.000000 | 1.333333 | 0.000000 | 5.333333 | 20.000000 | 35.000000 | 54.333333 | 0.333333 | 17.666667 | 1.666667 | 0.666667 | 15.000000 | 4.333333 | 0.666667 | 0.000000 | 14.000000 | 0.666667 | 0.000000 | 11.000000 | 3.000000 | 0.666667 | 14.666667 | 19.333333 | 0.000000 | 0.200000 | 0.833333 | NaN | 0.136364 | 0.833333 | 0.666667 | 0.322581 | 0.551020 | 0.000000 | 0.792453 | 0.400000 | 0.000000 | 0.733333 | 0.692308 | 1.000000 | 0.733333 | 0.355828 |
| 2316 | Tyrone Roberts | Orthodox | No | No | No | No | Tyrone Roberts | 0.000000 | 0.333333 | 0.000000 | 0.000000 | 1.594444 | 3.666667 | 1.000000 | 0.666667 | 4.000000 | 1.333333 | 0.000000 | 5.333333 | 20.000000 | 35.000000 | 54.333333 | 0.333333 | 17.666667 | 1.666667 | 0.666667 | 15.000000 | 4.333333 | 0.666667 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.011111 | 1.666667 | 1.666667 | 0.000000 | 1.000000 | 1.666667 | 0.666667 | 3.333333 | 10.333333 | 9.000000 | 16.333333 | 0.666667 | 8.333333 | 2.000000 | 0.000000 | 7.333333 | 2.000000 | 1.000000 | 0.666667 | 6.666667 | 0.333333 | 0.000000 | 6.333333 | 0.333333 | 0.333333 | 7.000000 | 7.333333 | 1.000000 | 0.207547 | 0.600000 | 1.000000 | 0.266667 | 0.307692 | 0.000000 | 0.266667 | 0.644172 | 1.000000 | 0.800000 | 0.166667 | NaN | 0.863636 | 0.166667 | 0.333333 | 0.677419 | 0.448980 |
2317 rows × 78 columns
records = pd.read_sql('SELECT * FROM records', connection)
records
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | Total_Rounds | Total_Fights | Wins | Losses | Draws | No_Contests | Win_by_KO_TKO | Win_by_Submission | Win_by_Decision | Loss_by_KO_TKO | Loss_by_Submission | Loss_by_Decision | Total_Fights_with_Title | Wins_with_Title | Losses_with_Title | Draws_with_Title | No_Contests_with_Title | Win_by_KO_TKO_with_Title | Win_by_Submission_with_Title | Win_by_Decision_with_Title | Loss_by_KO_TKO_with_Title | Loss_by_Submission_with_Title | Loss_by_Decision_with_Title | Win_% | Loss_% | KO_Win_% | Sub_Win_% | Dec_Win_% | KO_Loss_% | Sub_Loss_% | Dec_Loss_% | Title_Win_% | Title_Loss_% | Title_KO_Win_% | Title_Sub_Win_% | Title_Dec_Win_% | Title_KO_Loss_% | Title_Sub_Loss_% | Title_Dec_Loss_% | Rounds_per_Fight | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Asu Almabayev | None | No | No | No | No | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 3.000000 |
| 1 | CJ Vergara | Orthodox | No | No | No | No | 16 | 6 | 3 | 3 | 0 | 0 | 1 | 0 | 2 | 0 | 1 | 2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 50.000000 | 50.000000 | 16.666667 | 0.000000 | 33.333333 | 0.000000 | 16.666667 | 33.333333 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.666667 |
| 2 | Curtis Blaydes | Orthodox | No | No | No | No | 41 | 18 | 13 | 4 | 0 | 1 | 8 | 0 | 5 | 4 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 72.222222 | 22.222222 | 44.444444 | 0.000000 | 27.777778 | 22.222222 | 0.000000 | 0.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.277778 |
| 3 | Jailton Almeida | Orthodox | No | No | No | No | 13 | 7 | 6 | 1 | 0 | 0 | 2 | 3 | 1 | 1 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 85.714286 | 14.285714 | 28.571429 | 42.857143 | 14.285714 | 14.285714 | 0.000000 | 0.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.857143 |
| 4 | Benoit Saint Denis | Southpaw | No | No | No | No | 13 | 7 | 5 | 2 | 0 | 0 | 3 | 2 | 0 | 1 | 0 | 1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 71.428571 | 28.571429 | 42.857143 | 28.571429 | 0.000000 | 14.285714 | 0.000000 | 14.285714 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.857143 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2312 | Joao Roque | Orthodox | No | No | No | No | 3 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 3.000000 |
| 2313 | Marcelo Aguiar | Orthodox | No | No | No | No | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.000000 |
| 2314 | Adrian Serrano | None | No | No | No | No | 2 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.000000 |
| 2315 | David Dodd | Orthodox | No | No | No | No | 3 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 3.000000 |
| 2316 | Tyrone Roberts | Orthodox | No | No | No | No | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 3.000000 |
2317 rows × 46 columns
# Filter rows where 'Total_Fights' is greater than 3
min_fights = records[records['Total_Fights'] > 3]
# Create a list of fighters with more than 3 fights
fighters_list = min_fights['FIGHTER'].tolist()
len(fighters_list)
1321
heavyweight = pd.read_sql('SELECT * FROM heavyweight', connection)
light_heavyweight = pd.read_sql('SELECT * FROM light_heavyweight', connection)
middleweight = pd.read_sql('SELECT * FROM middleweight', connection)
welterweight = pd.read_sql('SELECT * FROM welterweight', connection)
lightweight = pd.read_sql('SELECT * FROM lightweight', connection)
featherweight = pd.read_sql('SELECT * FROM featherweight', connection)
bantamweight = pd.read_sql('SELECT * FROM bantamweight', connection)
flyweight = pd.read_sql('SELECT * FROM flyweight', connection)
women_featherweight = pd.read_sql('SELECT * FROM women_featherweight', connection)
women_bantamweight = pd.read_sql('SELECT * FROM women_bantamweight', connection)
women_flyweight = pd.read_sql('SELECT * FROM women_flyweight', connection)
women_strawweight = pd.read_sql('SELECT * FROM women_strawweight', connection)
# Create a new subset with the specified columns
df = master[['STANCE', 'RESULT', 'OPP_STANCE']]
# Remove rows where 'STANCE' or 'OPP_STANCE' is null
df = df.dropna(subset=['STANCE', 'OPP_STANCE'])
# Display the first few rows of the new subset to confirm
df
| STANCE | RESULT | OPP_STANCE | |
|---|---|---|---|
| 6 | Orthodox | Win | Orthodox |
| 7 | Orthodox | Win | Orthodox |
| 8 | Orthodox | Loss | Orthodox |
| 9 | Orthodox | Loss | Orthodox |
| 10 | Southpaw | Loss | Southpaw |
| ... | ... | ... | ... |
| 34579 | Orthodox | Loss | Orthodox |
| 34580 | Orthodox | Loss | Orthodox |
| 34581 | Orthodox | Win | Orthodox |
| 34582 | Orthodox | Win | Orthodox |
| 34583 | Orthodox | Win | Orthodox |
32810 rows × 3 columns
df = df[df['STANCE'] != df['OPP_STANCE']]
df = df[df['RESULT'].isin(['Win', 'Loss'])]
df
| STANCE | RESULT | OPP_STANCE | |
|---|---|---|---|
| 14 | Orthodox | Loss | Switch |
| 15 | Orthodox | Loss | Switch |
| 16 | Orthodox | Loss | Switch |
| 17 | Switch | Win | Orthodox |
| 18 | Switch | Win | Orthodox |
| ... | ... | ... | ... |
| 34555 | Orthodox | Loss | Southpaw |
| 34556 | Orthodox | Loss | Southpaw |
| 34557 | Orthodox | Loss | Southpaw |
| 34568 | Orthodox | Loss | Switch |
| 34569 | Switch | Win | Orthodox |
12178 rows × 3 columns
# Assuming df is your DataFrame after filtering to include only 'Win' and 'Loss' in 'RESULT'
result_counts = df.groupby(['STANCE', 'RESULT']).size().unstack(fill_value=0)
# Calculate the total fights per stance
result_counts['Total'] = result_counts.sum(axis=1)
# Calculate percentages
result_counts['Win_Percentage'] = (result_counts['Win'] / result_counts['Total']) * 100
result_counts['Loss_Percentage'] = (result_counts['Loss'] / result_counts['Total']) * 100
# You can then display this DataFrame
print(result_counts[['Win', 'Loss', 'Win_Percentage', 'Loss_Percentage']])
RESULT Win Loss Win_Percentage Loss_Percentage STANCE Orthodox 2705 3051 46.994441 53.005559 Southpaw 2635 2379 52.552852 47.447148 Switch 749 659 53.196023 46.803977
# Select specific columns and remove duplicates
subset_df = master[['BOUT', 'RESULT', 'FRAME_DELTA']].drop_duplicates().dropna(subset=['FRAME_DELTA'])
# Create ADVANTAGE column based on FRAME_DELTA
subset_df['ADVANTAGE'] = subset_df['FRAME_DELTA'].apply(lambda x: 'Yes' if x > 0 else 'No')
subset_df = subset_df[subset_df['RESULT'].isin(['Win', 'Loss'])]
# Output to verify the changes
subset_df
| BOUT | RESULT | FRAME_DELTA | ADVANTAGE | |
|---|---|---|---|---|
| 6 | Curtis Blaydes vs. Jailton Almeida | Win | 1.0 | Yes |
| 8 | Curtis Blaydes vs. Jailton Almeida | Loss | -1.0 | No |
| 10 | Dustin Poirier vs. Benoit Saint Denis | Loss | 1.5 | Yes |
| 12 | Dustin Poirier vs. Benoit Saint Denis | Win | -1.5 | No |
| 14 | Gilbert Burns vs. Jack Della Maddalena | Loss | -1.5 | No |
| ... | ... | ... | ... | ... |
| 34572 | Pat Miletich vs. John Alessio | Win | -1.0 | No |
| 34574 | Shonie Carter vs. Adrian Serrano | Loss | -2.0 | No |
| 34576 | Shonie Carter vs. Adrian Serrano | Win | 2.0 | Yes |
| 34578 | Tyrone Roberts vs. David Dodd | Loss | 5.0 | Yes |
| 34581 | Tyrone Roberts vs. David Dodd | Win | -5.0 | No |
13764 rows × 4 columns
# Assuming df is your DataFrame after filtering to include only 'Win' and 'Loss' in 'RESULT'
result_counts = subset_df.groupby(['ADVANTAGE', 'RESULT']).size().unstack(fill_value=0)
# Calculate the total fights per stance
result_counts['Total'] = result_counts.sum(axis=1)
# Calculate percentages
result_counts['Win_Percentage'] = (result_counts['Win'] / result_counts['Total']) * 100
result_counts['Loss_Percentage'] = (result_counts['Loss'] / result_counts['Total']) * 100
# You can then display this DataFrame
print(result_counts[['Win', 'Loss', 'Win_Percentage', 'Loss_Percentage']])
RESULT Win Loss Win_Percentage Loss_Percentage ADVANTAGE No 3503 3958 46.950811 53.049189 Yes 3379 2924 53.609392 46.390608
# Select specific columns and remove duplicates
subset_df = master[['BOUT', 'RESULT', 'APE_INDEX','HEIGHT_DELTA', 'REACH_DELTA', 'AGE_DELTA', 'FRAME_DELTA']].drop_duplicates()
subset_df
| BOUT | RESULT | APE_INDEX | HEIGHT_DELTA | REACH_DELTA | AGE_DELTA | FRAME_DELTA | |
|---|---|---|---|---|---|---|---|
| 0 | CJ Vergara vs. Asu Almabayev | Win | NaN | NaN | NaN | NaN | NaN |
| 3 | CJ Vergara vs. Asu Almabayev | Loss | 1.030303 | NaN | NaN | NaN | NaN |
| 6 | Curtis Blaydes vs. Jailton Almeida | Win | 1.052632 | 1.0 | 1.0 | 1.0 | 1.0 |
| 8 | Curtis Blaydes vs. Jailton Almeida | Loss | 1.053333 | -1.0 | -1.0 | -1.0 | -1.0 |
| 10 | Dustin Poirier vs. Benoit Saint Denis | Loss | 1.028169 | 2.0 | 1.0 | -7.0 | 1.5 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 34572 | Pat Miletich vs. John Alessio | Win | 1.000000 | 0.0 | -2.0 | 12.0 | -1.0 |
| 34574 | Shonie Carter vs. Adrian Serrano | Loss | 1.000000 | -2.0 | -2.0 | 8.0 | -2.0 |
| 34576 | Shonie Carter vs. Adrian Serrano | Win | 1.000000 | 2.0 | 2.0 | -8.0 | 2.0 |
| 34578 | Tyrone Roberts vs. David Dodd | Loss | 1.000000 | 5.0 | 5.0 | NaN | 5.0 |
| 34581 | Tyrone Roberts vs. David Dodd | Win | 1.000000 | -5.0 | -5.0 | NaN | -5.0 |
14488 rows × 7 columns
# Filter the dataframe where RESULT is 'Win'
win_df = subset_df[subset_df['RESULT'] == 'Win']
win_df
| BOUT | RESULT | APE_INDEX | HEIGHT_DELTA | REACH_DELTA | AGE_DELTA | FRAME_DELTA | |
|---|---|---|---|---|---|---|---|
| 0 | CJ Vergara vs. Asu Almabayev | Win | NaN | NaN | NaN | NaN | NaN |
| 6 | Curtis Blaydes vs. Jailton Almeida | Win | 1.052632 | 1.0 | 1.0 | 1.0 | 1.0 |
| 12 | Dustin Poirier vs. Benoit Saint Denis | Win | 1.043478 | -2.0 | -1.0 | 7.0 | -1.5 |
| 17 | Gilbert Burns vs. Jack Della Maddalena | Win | 1.028169 | 1.0 | 2.0 | -10.0 | 1.5 |
| 23 | Ion Cutelaba vs. Philipe Lins | Win | 1.054054 | 1.0 | 3.0 | 8.0 | 2.0 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 34558 | Kevin Randleman vs. Pedro Rizzo | Win | 1.000000 | -3.0 | -3.0 | 2.0 | -3.0 |
| 34569 | Matt Hughes vs. Marcelo Aguiar | Win | 1.057971 | -1.0 | 3.0 | NaN | 1.0 |
| 34572 | Pat Miletich vs. John Alessio | Win | 1.000000 | 0.0 | -2.0 | 12.0 | -1.0 |
| 34576 | Shonie Carter vs. Adrian Serrano | Win | 1.000000 | 2.0 | 2.0 | -8.0 | 2.0 |
| 34581 | Tyrone Roberts vs. David Dodd | Win | 1.000000 | -5.0 | -5.0 | NaN | -5.0 |
7112 rows × 7 columns
win_df.describe()
| APE_INDEX | HEIGHT_DELTA | REACH_DELTA | AGE_DELTA | FRAME_DELTA | |
|---|---|---|---|---|---|
| count | 6986.000000 | 6901.000000 | 6901.000000 | 6954.000000 | 6901.000000 |
| mean | 1.026002 | 0.114621 | 0.373859 | -0.793069 | 0.244240 |
| std | 0.027321 | 2.540008 | 3.301459 | 5.194162 | 2.654312 |
| min | 0.920635 | -13.000000 | -12.000000 | -18.000000 | -12.500000 |
| 25% | 1.000000 | -1.000000 | -2.000000 | -4.000000 | -1.500000 |
| 50% | 1.027397 | 0.000000 | 0.000000 | -1.000000 | 0.000000 |
| 75% | 1.043478 | 2.000000 | 2.000000 | 3.000000 | 2.000000 |
| max | 1.120000 | 12.000000 | 13.000000 | 16.000000 | 11.500000 |
# Remove rows with any missing delta values
win_df = subset_df.dropna(subset=['APE_INDEX','HEIGHT_DELTA', 'REACH_DELTA', 'AGE_DELTA', 'FRAME_DELTA'])
# Convert 'RESULT' to binary (1 for 'Win', 0 otherwise)
win_df['RESULT_BINARY'] = (win_df['RESULT'] == 'Win').astype(int)
y = win_df['RESULT_BINARY']
# Initialize a dictionary to store the accuracy of each model
accuracy_dict = {}
# Loop through each delta column and perform logistic regression individually
for delta_column in ['APE_INDEX','HEIGHT_DELTA', 'REACH_DELTA', 'AGE_DELTA', 'FRAME_DELTA']:
# Select the column as the predictor
X = win_df[[delta_column]]
# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Train the model
logreg = LogisticRegression()
logreg.fit(X_train, y_train)
# Predict and calculate accuracy
y_pred = logreg.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
# Store the accuracy
accuracy_dict[delta_column] = accuracy
accuracy_dict
/var/folders/_6/_1qflrx12_j4kc3ssfs46ldw0000gn/T/ipykernel_1785/1180037860.py:5: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy win_df['RESULT_BINARY'] = (win_df['RESULT'] == 'Win').astype(int)
{'APE_INDEX': 0.5348419540229885,
'HEIGHT_DELTA': 0.5255028735632183,
'REACH_DELTA': 0.5427442528735632,
'AGE_DELTA': 0.5520833333333334,
'FRAME_DELTA': 0.5427442528735632}
# Set up the figure and axes for plotting
fig, axs = plt.subplots(3, 2, figsize=(14, 18))
axs = axs.flatten() # Flatten the array of axes for easy iteration
# Loop through each delta column and plot
for i, delta_column in enumerate(['APE_INDEX', 'HEIGHT_DELTA', 'REACH_DELTA', 'AGE_DELTA', 'FRAME_DELTA']):
# Prepare the data
X = win_df[[delta_column]]
y = win_df['RESULT_BINARY']
# Fit the logistic regression model
logreg = LogisticRegression()
logreg.fit(X, y)
# Generate a sequence of values for the predictor variable
X_test = np.linspace(X.min(), X.max(), 300)
# Predict probabilities for the test sequence
y_test_pred = logreg.predict_proba(X_test)[:, 1]
# Plot
axs[i].scatter(X, y, color='blue', alpha=0.2, label='Data Points')
axs[i].plot(X_test, y_test_pred, color='red', linewidth=2, label='Logistic Regression Curve')
axs[i].set_xlabel(f'{delta_column}')
axs[i].set_ylabel('Probability of Win')
axs[i].set_title(f'Logistic Regression for {delta_column}')
axs[i].legend()
# Adjust layout and display the plots
plt.tight_layout()
plt.show()
/opt/anaconda3/lib/python3.9/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names warnings.warn( /opt/anaconda3/lib/python3.9/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names warnings.warn( /opt/anaconda3/lib/python3.9/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names warnings.warn( /opt/anaconda3/lib/python3.9/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names warnings.warn( /opt/anaconda3/lib/python3.9/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names warnings.warn(
# Selecting specific columns and dropping duplicates and null values
age_df = master[['BOUT', 'RESULT', 'AGE']].drop_duplicates().dropna()
# Filter where RESULT is either 'Win' or 'Loss'
age_df = age_df[age_df['RESULT'].isin(['Win', 'Loss'])]
# Display
age_df
| BOUT | RESULT | AGE | |
|---|---|---|---|
| 3 | CJ Vergara vs. Asu Almabayev | Loss | 32.0 |
| 6 | Curtis Blaydes vs. Jailton Almeida | Win | 33.0 |
| 8 | Curtis Blaydes vs. Jailton Almeida | Loss | 32.0 |
| 10 | Dustin Poirier vs. Benoit Saint Denis | Loss | 28.0 |
| 12 | Dustin Poirier vs. Benoit Saint Denis | Win | 35.0 |
| ... | ... | ... | ... |
| 34570 | Pat Miletich vs. John Alessio | Loss | 20.0 |
| 34572 | Pat Miletich vs. John Alessio | Win | 32.0 |
| 34574 | Shonie Carter vs. Adrian Serrano | Loss | 36.0 |
| 34576 | Shonie Carter vs. Adrian Serrano | Win | 28.0 |
| 34578 | Tyrone Roberts vs. David Dodd | Loss | 26.0 |
14141 rows × 3 columns
age_df.describe()
| AGE | |
|---|---|
| count | 14141.000000 |
| mean | 29.780567 |
| std | 4.134685 |
| min | 18.000000 |
| 25% | 27.000000 |
| 50% | 30.000000 |
| 75% | 32.000000 |
| max | 47.000000 |
# Grouping the data by AGE and RESULT and counting occurrences
age_result_count = age_df.groupby(['AGE', 'RESULT']).size().unstack(fill_value=0)
# Plotting
plt.figure(figsize=(14, 7))
for result in age_result_count.columns:
plt.plot(age_result_count.index, age_result_count[result], label=result)
plt.title('Count of Wins and Losses Across Age')
plt.xlabel('Age')
plt.ylabel('Count')
plt.legend(title='Result')
plt.grid(True)
plt.show()
# Select specific columns and remove duplicates
subset_df = master[['BOUT', 'RESULT', 'APE_INDEX','HEIGHT_DELTA', 'REACH_DELTA', 'AGE_DELTA', 'FRAME_DELTA']].drop_duplicates()
subset_df
| BOUT | RESULT | APE_INDEX | HEIGHT_DELTA | REACH_DELTA | AGE_DELTA | FRAME_DELTA | |
|---|---|---|---|---|---|---|---|
| 0 | CJ Vergara vs. Asu Almabayev | Win | NaN | NaN | NaN | NaN | NaN |
| 3 | CJ Vergara vs. Asu Almabayev | Loss | 1.030303 | NaN | NaN | NaN | NaN |
| 6 | Curtis Blaydes vs. Jailton Almeida | Win | 1.052632 | 1.0 | 1.0 | 1.0 | 1.0 |
| 8 | Curtis Blaydes vs. Jailton Almeida | Loss | 1.053333 | -1.0 | -1.0 | -1.0 | -1.0 |
| 10 | Dustin Poirier vs. Benoit Saint Denis | Loss | 1.028169 | 2.0 | 1.0 | -7.0 | 1.5 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 34572 | Pat Miletich vs. John Alessio | Win | 1.000000 | 0.0 | -2.0 | 12.0 | -1.0 |
| 34574 | Shonie Carter vs. Adrian Serrano | Loss | 1.000000 | -2.0 | -2.0 | 8.0 | -2.0 |
| 34576 | Shonie Carter vs. Adrian Serrano | Win | 1.000000 | 2.0 | 2.0 | -8.0 | 2.0 |
| 34578 | Tyrone Roberts vs. David Dodd | Loss | 1.000000 | 5.0 | 5.0 | NaN | 5.0 |
| 34581 | Tyrone Roberts vs. David Dodd | Win | 1.000000 | -5.0 | -5.0 | NaN | -5.0 |
14488 rows × 7 columns
win_df = subset_df[subset_df['RESULT'] == 'Win']
win_df
| BOUT | RESULT | APE_INDEX | HEIGHT_DELTA | REACH_DELTA | AGE_DELTA | FRAME_DELTA | |
|---|---|---|---|---|---|---|---|
| 0 | CJ Vergara vs. Asu Almabayev | Win | NaN | NaN | NaN | NaN | NaN |
| 6 | Curtis Blaydes vs. Jailton Almeida | Win | 1.052632 | 1.0 | 1.0 | 1.0 | 1.0 |
| 12 | Dustin Poirier vs. Benoit Saint Denis | Win | 1.043478 | -2.0 | -1.0 | 7.0 | -1.5 |
| 17 | Gilbert Burns vs. Jack Della Maddalena | Win | 1.028169 | 1.0 | 2.0 | -10.0 | 1.5 |
| 23 | Ion Cutelaba vs. Philipe Lins | Win | 1.054054 | 1.0 | 3.0 | 8.0 | 2.0 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 34558 | Kevin Randleman vs. Pedro Rizzo | Win | 1.000000 | -3.0 | -3.0 | 2.0 | -3.0 |
| 34569 | Matt Hughes vs. Marcelo Aguiar | Win | 1.057971 | -1.0 | 3.0 | NaN | 1.0 |
| 34572 | Pat Miletich vs. John Alessio | Win | 1.000000 | 0.0 | -2.0 | 12.0 | -1.0 |
| 34576 | Shonie Carter vs. Adrian Serrano | Win | 1.000000 | 2.0 | 2.0 | -8.0 | 2.0 |
| 34581 | Tyrone Roberts vs. David Dodd | Win | 1.000000 | -5.0 | -5.0 | NaN | -5.0 |
7112 rows × 7 columns
# Creating histograms for the desired columns
fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(18, 10))
fig.subplots_adjust(hspace=0.5)
columns = ['APE_INDEX', 'HEIGHT_DELTA', 'REACH_DELTA', 'AGE_DELTA', 'FRAME_DELTA']
for ax, column in zip(axes.flatten(), columns):
ax.hist(win_df[column].dropna(), bins=20, color='green', alpha=0.7) # Adjust bin size as necessary
ax.set_title(f'Histogram of {column}')
ax.set_xlabel(column)
ax.set_ylabel('Frequency')
# Remove the empty subplot (if there are an odd number of columns)
fig.delaxes(axes[1][2])
plt.show()
# Create a figure and axis objects
fig, axes = plt.subplots(nrows=len(columns), ncols=1, figsize=(10, 15))
# Plotting histograms
for i, col in enumerate(columns):
axes[i].hist(win_df[col].dropna(), bins=15, color='green', alpha=0.7)
axes[i].set_title(f'Histogram of {col}')
axes[i].set_xlabel(col)
axes[i].set_ylabel('Frequency')
# Adjust layout
plt.tight_layout()
plt.show()
# Assuming df is your DataFrame
loss_df = subset_df[subset_df['RESULT'] == 'Loss']
loss_df
| BOUT | RESULT | APE_INDEX | HEIGHT_DELTA | REACH_DELTA | AGE_DELTA | FRAME_DELTA | |
|---|---|---|---|---|---|---|---|
| 3 | CJ Vergara vs. Asu Almabayev | Loss | 1.030303 | NaN | NaN | NaN | NaN |
| 8 | Curtis Blaydes vs. Jailton Almeida | Loss | 1.053333 | -1.0 | -1.0 | -1.0 | -1.0 |
| 10 | Dustin Poirier vs. Benoit Saint Denis | Loss | 1.028169 | 2.0 | 1.0 | -7.0 | 1.5 |
| 14 | Gilbert Burns vs. Jack Della Maddalena | Loss | 1.014286 | -1.0 | -2.0 | 10.0 | -1.5 |
| 20 | Ion Cutelaba vs. Philipe Lins | Loss | 1.027397 | -1.0 | -3.0 | -8.0 | -2.0 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 34563 | Kevin Randleman vs. Pedro Rizzo | Loss | 1.000000 | 3.0 | 3.0 | -2.0 | 3.0 |
| 34568 | Matt Hughes vs. Marcelo Aguiar | Loss | 1.000000 | 1.0 | -3.0 | NaN | -1.0 |
| 34570 | Pat Miletich vs. John Alessio | Loss | 1.028571 | 0.0 | 2.0 | -12.0 | 1.0 |
| 34574 | Shonie Carter vs. Adrian Serrano | Loss | 1.000000 | -2.0 | -2.0 | 8.0 | -2.0 |
| 34578 | Tyrone Roberts vs. David Dodd | Loss | 1.000000 | 5.0 | 5.0 | NaN | 5.0 |
7114 rows × 7 columns
# Creating histograms for the desired columns
fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(18, 10))
fig.subplots_adjust(hspace=0.5)
columns = ['APE_INDEX', 'HEIGHT_DELTA', 'REACH_DELTA', 'AGE_DELTA', 'FRAME_DELTA']
for ax, column in zip(axes.flatten(), columns):
ax.hist(loss_df[column].dropna(), bins=20, color='red', alpha=0.7) # Adjust bin size as necessary
ax.set_title(f'Histogram of {column}')
ax.set_xlabel(column)
ax.set_ylabel('Frequency')
# Remove the empty subplot (if there are an odd number of columns)
fig.delaxes(axes[1][2])
plt.show()
# Filtering out 'No Contest' and 'Draw' from the 'RESULT' column
filtered_subset_df = subset_df[~subset_df['RESULT'].isin(['No Contest', 'Draw'])]
# Creating the histograms again with the filtered data
fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(14, 18))
axes = axes.flatten() # Flatten to 1D array for easy iteration
# Plotting histograms for each column, excluding 'No Contest' and 'Draw' results
for i, column in enumerate(columns):
sns.histplot(data=filtered_subset_df, x=column, hue="RESULT", element="step", stat="density", common_norm=False, ax=axes[i])
axes[i].set_title(f'Distribution of {column} by Result')
axes[i].set_xlabel(column)
axes[i].set_ylabel('Density')
# Remove the last empty subplot (if any)
if len(columns) < len(axes):
fig.delaxes(axes[-1])
plt.tight_layout()
plt.show()
fb.sort_values(by='Buyrate', ascending=False, inplace = True)
fb.reset_index(drop=True, inplace = True)
# fb.index = range(1, len(fb) + 1)
fb
/var/folders/_6/_1qflrx12_j4kc3ssfs46ldw0000gn/T/ipykernel_1785/741843745.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy fb.sort_values(by='Buyrate', ascending=False, inplace = True)
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | Buyrate | Avg_Buyrate_Per_Event | PPV_Fights | Buyrate_PERCENTILE | PPV_Fights_PERCENTILE | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Conor McGregor | Southpaw | Yes | Yes | Yes | Yes | 13555166.0 | 1.355517e+06 | 10.0 | 100.000000 | 94.283647 |
| 1 | Georges St-Pierre | Orthodox | Yes | Yes | Yes | Yes | 12430000.0 | 5.650000e+05 | 22.0 | 99.956841 | 99.927641 |
| 2 | Jim Miller | Southpaw | No | No | No | No | 11741000.0 | 6.522778e+05 | 18.0 | 99.913681 | 99.493488 |
| 3 | Jon Jones | Orthodox | Yes | Yes | Yes | Yes | 10992000.0 | 6.465882e+05 | 17.0 | 99.870522 | 99.204052 |
| 4 | Demian Maia | Southpaw | No | No | Yes | No | 10907000.0 | 5.453500e+05 | 20.0 | 99.827363 | 99.710564 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1377 | Chris Brennan | Orthodox | No | No | No | No | 35000.0 | 3.500000e+04 | 1.0 | 40.483384 | 20.260492 |
| 1378 | Kevin Randleman | Orthodox | Yes | No | Yes | Yes | 35000.0 | 3.500000e+04 | 1.0 | 40.483384 | 20.260492 |
| 1379 | Sean Alvarez | Orthodox | No | No | No | No | 35000.0 | 3.500000e+04 | 1.0 | 40.483384 | 20.260492 |
| 1380 | Richard Crunkilton | Orthodox | No | No | No | No | 35000.0 | 3.500000e+04 | 1.0 | 40.483384 | 20.260492 |
| 1381 | Romie Aram | Orthodox | No | No | No | No | 35000.0 | 3.500000e+04 | 1.0 | 40.483384 | 20.260492 |
1382 rows × 11 columns
fb.describe()
| Buyrate | Avg_Buyrate_Per_Event | PPV_Fights | Buyrate_PERCENTILE | PPV_Fights_PERCENTILE | |
|---|---|---|---|---|---|
| count | 1.382000e+03 | 1.382000e+03 | 1382.000000 | 1382.000000 | 1382.000000 |
| mean | 1.574689e+06 | 4.843432e+05 | 3.251085 | 70.198533 | 50.036179 |
| std | 1.796960e+06 | 3.025672e+05 | 3.343249 | 17.223849 | 27.769294 |
| min | 3.500000e+04 | 3.500000e+04 | 1.000000 | 40.483384 | 20.260492 |
| 25% | 4.000000e+05 | 2.904444e+05 | 1.000000 | 55.135952 | 20.260492 |
| 50% | 9.150000e+05 | 4.400000e+05 | 2.000000 | 70.198533 | 49.963821 |
| 75% | 2.040000e+06 | 6.150000e+05 | 4.000000 | 85.088476 | 74.963821 |
| max | 1.355517e+07 | 2.400000e+06 | 22.000000 | 100.000000 | 99.927641 |
fb.corr()
| Buyrate | Avg_Buyrate_Per_Event | PPV_Fights | Buyrate_PERCENTILE | PPV_Fights_PERCENTILE | |
|---|---|---|---|---|---|
| Buyrate | 1.000000 | 0.314592 | 0.894954 | 0.816858 | 0.744467 |
| Avg_Buyrate_Per_Event | 0.314592 | 1.000000 | 0.000047 | 0.532203 | -0.001989 |
| PPV_Fights | 0.894954 | 0.000047 | 1.000000 | 0.717016 | 0.825409 |
| Buyrate_PERCENTILE | 0.816858 | 0.532203 | 0.717016 | 1.000000 | 0.812387 |
| PPV_Fights_PERCENTILE | 0.744467 | -0.001989 | 0.825409 | 0.812387 | 1.000000 |
# Group the data by 'EITHER_CHAMP' and calculate the total buyrate for each category
champ_summary = fb.groupby('EITHER_CHAMP')['Buyrate'].sum().reset_index()
# Calculate the total buyrate to find out the percentage contribution of each category
total_buyrate = champ_summary['Buyrate'].sum()
champ_summary['Percentage'] = (champ_summary['Buyrate'] / total_buyrate) * 100
champ_summary
| EITHER_CHAMP | Buyrate | Percentage | |
|---|---|---|---|
| 0 | No | 1.750347e+09 | 80.430609 |
| 1 | Yes | 4.258729e+08 | 19.569391 |
total_buyrate
2176219652.0
# Group the data by 'EVER_CHALLENGER' and calculate the total buyrate for each category
challenger_summary = fb.groupby('EVER_CHALLENGER')['Buyrate'].sum().reset_index()
# Calculate the total buyrate to find out the percentage contribution of each category
total_buyrate = champ_summary['Buyrate'].sum()
challenger_summary['Percentage'] = (challenger_summary['Buyrate'] / total_buyrate) * 100
challenger_summary
| EVER_CHALLENGER | Buyrate | Percentage | |
|---|---|---|---|
| 0 | No | 1.435586e+09 | 65.966973 |
| 1 | Yes | 7.406334e+08 | 34.033027 |
# Group the data by 'STANCE' and calculate the total buyrate for each category
stance_summary = fb.groupby('STANCE')['Buyrate'].sum().reset_index()
# Calculate the total buyrate to find out the percentage contribution of each category
total_buyrate = champ_summary['Buyrate'].sum()
stance_summary['Percentage'] = (stance_summary['Buyrate'] / total_buyrate) * 100
stance_summary
| STANCE | Buyrate | Percentage | |
|---|---|---|---|
| 0 | Orthodox | 1.643317e+09 | 75.512465 |
| 1 | Southpaw | 4.469722e+08 | 20.538929 |
| 2 | Switch | 8.104633e+07 | 3.724180 |
# Correcting the column name and plotting again
plt.figure(figsize=(10, 6))
plt.scatter(fb['PPV_Fights'], fb['Buyrate'], alpha=0.5)
plt.title('Scatter Plot of Buyrate vs. PPV Fights')
plt.xlabel('PPV Fights')
plt.ylabel('Buyrate')
plt.grid(True)
plt.show()
# Using seaborn to plot the scatter plot with a regression line
plt.figure(figsize=(10, 6))
sns.regplot(x='PPV_Fights', y='Buyrate', data=fb, scatter_kws={'alpha':0.5})
plt.title('Scatter Plot of Buyrate vs. PPV Fights with Regression Line')
plt.xlabel('PPV Fights')
plt.ylabel('Buyrate')
plt.grid(True)
plt.show()
# Clean the data: Remove rows with NaN in either 'Buyrate' or 'PPV_Fights'
df_cleaned = fb.dropna(subset=['Buyrate', 'PPV_Fights'])
# Defining the dependent variable (y) and independent variable (x)
y = df_cleaned['Buyrate'].values.reshape(-1, 1)
x = df_cleaned['PPV_Fights'].values.reshape(-1, 1)
# Linear Regression
model = LinearRegression()
model.fit(x, y)
# Coefficients
slope = model.coef_[0]
intercept = model.intercept_
# Calculate R-squared
r_squared = model.score(x, y)
slope[0], intercept[0], r_squared
(481028.1468489902, 10825.027646517381, 0.8009435134391198)
# Predict y values for the given x values to plot the regression line
y_pred = model.predict(x)
# Plotting
plt.figure(figsize=(10, 6))
plt.scatter(x, y, color='blue', alpha=0.5, label='Actual Buyrate')
plt.plot(x, y_pred, color='red', label='Regression Line')
plt.title('Linear Regression: Buyrate vs PPV_Fights')
plt.xlabel('PPV_Fights')
plt.ylabel('Buyrate')
plt.legend()
plt.grid(True)
plt.show()
# Creating dummy variables for the 'STANCE' column
df_with_dummies = pd.get_dummies(df_cleaned, columns=['STANCE'], drop_first=True)
# Defining the new independent variables including the stance dummies
X_multiple = df_with_dummies[['PPV_Fights', 'STANCE_Southpaw', 'STANCE_Switch']]
# Multiple Linear Regression
model_multiple = LinearRegression()
model_multiple.fit(X_multiple, y)
# Coefficients and Intercept
coefficients = model_multiple.coef_[0]
intercept_multiple = model_multiple.intercept_
# Calculate R-squared for the multiple regression
r_squared_multiple = model_multiple.score(X_multiple, y)
coefficients, intercept_multiple[0], r_squared_multiple
(array([480994.16146437, 33376.03748113, 129287.79240106]), -153.882503160974, 0.8011702621643987)
# Separate data by stance for color-coding
southpaw = df_with_dummies[df_with_dummies['STANCE_Southpaw'] == 1]
switch = df_with_dummies[df_with_dummies['STANCE_Switch'] == 1]
orthodox = df_with_dummies[(df_with_dummies['STANCE_Southpaw'] == 0) & (df_with_dummies['STANCE_Switch'] == 0)]
# Prepare figure
plt.figure(figsize=(10, 6))
# Scatter plot for each stance
plt.scatter(orthodox['PPV_Fights'], orthodox['Buyrate'], color='green', alpha=0.5, label='Orthodox')
plt.scatter(southpaw['PPV_Fights'], southpaw['Buyrate'], color='blue', alpha=0.5, label='Southpaw')
plt.scatter(switch['PPV_Fights'], switch['Buyrate'], color='red', alpha=0.5, label='Switch')
# Add a legend and labels
plt.title('Buyrate vs PPV_Fights by Stance')
plt.xlabel('PPV_Fights')
plt.ylabel('Buyrate')
plt.legend()
plt.grid(True)
plt.show()
# For simplicity, let's calculate and display regression lines for each stance category independently
# This involves fitting a simple linear model for each stance category with respect to PPV_Fights
# Initialize models for each stance
model_orthodox = LinearRegression()
model_southpaw = LinearRegression()
model_switch = LinearRegression()
# Fit the models
model_orthodox.fit(orthodox['PPV_Fights'].values.reshape(-1, 1), orthodox['Buyrate'])
model_southpaw.fit(southpaw['PPV_Fights'].values.reshape(-1, 1), southpaw['Buyrate'])
model_switch.fit(switch['PPV_Fights'].values.reshape(-1, 1), switch['Buyrate'])
# Generate predictions for each stance category for a smooth line
x_range = np.linspace(df_with_dummies['PPV_Fights'].min(), df_with_dummies['PPV_Fights'].max(), 100).reshape(-1, 1)
y_pred_orthodox = model_orthodox.predict(x_range)
y_pred_southpaw = model_southpaw.predict(x_range)
y_pred_switch = model_switch.predict(x_range)
# Plotting
plt.figure(figsize=(10, 6))
# Actual data points
plt.scatter(orthodox['PPV_Fights'], orthodox['Buyrate'], color='green', alpha=0.5, label='Orthodox')
plt.scatter(southpaw['PPV_Fights'], southpaw['Buyrate'], color='blue', alpha=0.5, label='Southpaw')
plt.scatter(switch['PPV_Fights'], switch['Buyrate'], color='red', alpha=0.5, label='Switch')
# Regression lines
plt.plot(x_range, y_pred_orthodox, color='green', linestyle='--', label='Orthodox Regression Line')
plt.plot(x_range, y_pred_southpaw, color='blue', linestyle='--', label='Southpaw Regression Line')
plt.plot(x_range, y_pred_switch, color='red', linestyle='--', label='Switch Regression Line')
plt.title('Buyrate vs PPV_Fights by Stance with Regression Lines')
plt.xlabel('PPV_Fights')
plt.ylabel('Buyrate')
plt.legend()
plt.grid(True)
plt.show()
# Filter out rows where necessary data is missing
df_filtered = fb.dropna(subset=['Buyrate', 'PPV_Fights'])
# Define the features and target variable
X = df_filtered[['EITHER_CHAMP', 'PPV_Fights']]
y = df_filtered['Buyrate']
# Encoding categorical data & Preparing the pipeline for linear regression
categorical_features = ['EITHER_CHAMP']
numerical_features = ['PPV_Fights']
preprocessor = ColumnTransformer(
transformers=[
('num', 'passthrough', numerical_features),
('cat', OneHotEncoder(), categorical_features)
])
regression_pipeline = Pipeline(steps=[('preprocessor', preprocessor),
('regressor', LinearRegression())])
# Splitting the dataset into the Training set and Test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
# Fitting Multiple Linear Regression to the Training set
regression_pipeline.fit(X_train, y_train)
# Predicting the Test set results
y_pred = regression_pipeline.predict(X_test)
# Calculating the performance metrics
r2 = r2_score(y_test, y_pred)
mse = mean_squared_error(y_test, y_pred)
r2, mse
(0.848032301446808, 645916621186.0352)
# Set a larger plot size
plt.figure(figsize=(10, 6))
# Create separate dataframes for 'EITHER_CHAMP' 'Yes' and 'No'
df_champ_yes = df_filtered[df_filtered['EITHER_CHAMP'] == 'Yes']
df_champ_no = df_filtered[df_filtered['EITHER_CHAMP'] == 'No']
# Plotting 'EITHER_CHAMP' 'Yes'
sns.scatterplot(x='PPV_Fights', y='Buyrate', data=df_champ_yes, color='gold', label='EITHER_CHAMP: Yes')
# Plotting regression line for 'EITHER_CHAMP' 'Yes'
sns.regplot(x='PPV_Fights', y='Buyrate', data=df_champ_yes, scatter=False, color='gold')
# Plotting 'EITHER_CHAMP' 'No'
sns.scatterplot(x='PPV_Fights', y='Buyrate', data=df_champ_no, color='silver', label='EITHER_CHAMP: No')
# Plotting regression line for 'EITHER_CHAMP' 'No'
sns.regplot(x='PPV_Fights', y='Buyrate', data=df_champ_no, scatter=False, color='silver')
plt.xlabel('PPV Fights')
plt.ylabel('Buyrate')
plt.title('Buyrate vs PPV Fights with Regression Lines for EITHER_CHAMP Yes and No')
plt.legend()
plt.show()
# Filter out rows where necessary data is missing
df_filtered = fb.dropna(subset=['Buyrate', 'PPV_Fights'])
# Define the features and target variable
X = df_filtered[['EVER_CHALLENGER', 'PPV_Fights']]
y = df_filtered['Buyrate']
# Encoding categorical data & Preparing the pipeline for linear regression
categorical_features = ['EVER_CHALLENGER']
numerical_features = ['PPV_Fights']
preprocessor = ColumnTransformer(
transformers=[
('num', 'passthrough', numerical_features),
('cat', OneHotEncoder(), categorical_features)
])
regression_pipeline = Pipeline(steps=[('preprocessor', preprocessor),
('regressor', LinearRegression())])
# Splitting the dataset into the Training set and Test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
# Fitting Multiple Linear Regression to the Training set
regression_pipeline.fit(X_train, y_train)
# Predicting the Test set results
y_pred = regression_pipeline.predict(X_test)
# Calculating the performance metrics
r2 = r2_score(y_test, y_pred)
mse = mean_squared_error(y_test, y_pred)
r2, mse
(0.8597258317738283, 596214969647.7537)
# Set a larger plot size
plt.figure(figsize=(10, 6))
# Create separate dataframes for 'EITHER_CHAMP' 'Yes' and 'No'
df_challenger_yes = df_filtered[df_filtered['EVER_CHALLENGER'] == 'Yes']
df_challenger_no = df_filtered[df_filtered['EVER_CHALLENGER'] == 'No']
# Plotting 'EITHER_CHAMP' 'Yes'
sns.scatterplot(x='PPV_Fights', y='Buyrate', data=df_champ_yes, color='aqua', label='EVER_CHALLENGER: Yes')
# Plotting regression line for 'EITHER_CHAMP' 'Yes'
sns.regplot(x='PPV_Fights', y='Buyrate', data=df_champ_yes, scatter=False, color='aqua')
# Plotting 'EITHER_CHAMP' 'No'
sns.scatterplot(x='PPV_Fights', y='Buyrate', data=df_champ_no, color='violet', label='EVER_CHALLENGER: No')
# Plotting regression line for 'EITHER_CHAMP' 'No'
sns.regplot(x='PPV_Fights', y='Buyrate', data=df_champ_no, scatter=False, color='violet')
plt.xlabel('PPV Fights')
plt.ylabel('Buyrate')
plt.title('Buyrate vs PPV Fights with Regression Lines for EVER_CHALLENGER Yes and No')
plt.legend()
plt.show()
# Calculating the total cumulative buyrate
total_cumulative_buyrate = fb['Buyrate'].sum()
total_cumulative_buyrate
2176219652.0
# Calculate mean and median buyrate for each stance
buyrate_stats = fb.groupby('STANCE')['Buyrate'].agg(['mean', 'median'])
# Display the statistics
buyrate_stats
| mean | median | |
|---|---|---|
| STANCE | ||
| Orthodox | 1.538686e+06 | 892500.0 |
| Southpaw | 1.787889e+06 | 1000000.0 |
| Switch | 1.500858e+06 | 1245000.0 |
# Prepare data for plotting for the stances
plot_data_stance = buyrate_stats.T # Transpose to get categories as columns
# Plotting for stances
plot_data_stance.plot(kind='bar', figsize=(10, 6))
plt.title('Mean and Median Buyrate for Each Fighter Stance')
plt.ylabel('Buyrate')
plt.xlabel('Statistic')
plt.xticks(rotation=0)
plt.legend(title='STANCE')
plt.tight_layout()
# Show the plot
plt.show()
# Calculate mean and median ppv fights
ppv_fight1 = fb.groupby('EITHER_CHAMP')['PPV_Fights'].agg(['mean', 'median'])
# Display the statistics
ppv_fight1
| mean | median | |
|---|---|---|
| EITHER_CHAMP | ||
| No | 2.822014 | 2.0 |
| Yes | 8.693069 | 8.0 |
# Calculate mean and median buyrate
buyrate_stats1 = fb.groupby('EITHER_CHAMP')['Buyrate'].agg(['mean', 'median'])
# Display the statistics
buyrate_stats1
| mean | median | |
|---|---|---|
| EITHER_CHAMP | ||
| No | 1.366391e+06 | 840000.0 |
| Yes | 4.216564e+06 | 3713000.0 |
# Calculate mean and median buyrate
buyrate_percent1 = fb.groupby('EITHER_CHAMP')['Buyrate_PERCENTILE'].agg(['mean', 'median'])
# Display the statistics
buyrate_percent1
| mean | median | |
|---|---|---|
| EITHER_CHAMP | ||
| No | 68.805678 | 68.580060 |
| Yes | 87.864343 | 93.655589 |
# Prepare data for plotting
plot_data = buyrate_stats1.T # Transpose to get categories as columns
# Plotting
plot_data.plot(kind='bar', figsize=(10, 6))
plt.title('Mean and Median Buyrate for Champion vs Non-champion Fighters')
plt.ylabel('Buyrate')
plt.xlabel('Statistic')
plt.xticks(rotation=0)
plt.legend(title='EITHER_CHAMP')
plt.tight_layout()
# Show the plot
plt.show()
# Calculate mean and median ppv fights
ppv_fight2 = fb.groupby('EVER_CHALLENGER')['PPV_Fights'].agg(['mean', 'median'])
# Display the statistics
ppv_fight2
| mean | median | |
|---|---|---|
| EVER_CHALLENGER | ||
| No | 2.527350 | 2.0 |
| Yes | 7.245283 | 6.0 |
# Calculate mean and median buyrate
buyrate_stats2 = fb.groupby('EVER_CHALLENGER')['Buyrate'].agg(['mean', 'median'])
# Display the statistics
buyrate_stats2
| mean | median | |
|---|---|---|
| EVER_CHALLENGER | ||
| No | 1.226997e+06 | 787500.0 |
| Yes | 3.493554e+06 | 2789500.0 |
# Calculate mean and median buyrate
buyrate_percent1 = fb.groupby('EVER_CHALLENGER')['Buyrate_PERCENTILE'].agg(['mean', 'median'])
# Display the statistics
buyrate_percent1
| mean | median | |
|---|---|---|
| EVER_CHALLENGER | ||
| No | 67.492761 | 66.832110 |
| Yes | 85.131330 | 89.900734 |
# Prepare data for plotting
plot_data = buyrate_stats1.T # Transpose to get categories as columns
# Plotting
plot_data.plot(kind='bar', figsize=(10, 6))
plt.title('Mean and Median Buyrate for Challenger vs Non-challenger Fighters')
plt.ylabel('Buyrate')
plt.xlabel('Statistic')
plt.xticks(rotation=0)
plt.legend(title='EVER_CHALLENGER')
plt.tight_layout()
# Show the plot
plt.show()
# Calculate the number of entries that represent the top 10% of the dataset
top_10_percent_count = int(len(fb) * 0.1)
# Sort the dataset by 'Buyrate' descending and take the top 10%
top_10_percent_earners = fb.sort_values(by='Buyrate', ascending=False).head(top_10_percent_count)
top_10_percent_earners
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | Buyrate | Avg_Buyrate_Per_Event | PPV_Fights | Buyrate_PERCENTILE | PPV_Fights_PERCENTILE | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Conor McGregor | Southpaw | Yes | Yes | Yes | Yes | 13555166.0 | 1.355517e+06 | 10.0 | 100.000000 | 94.283647 |
| 1 | Georges St-Pierre | Orthodox | Yes | Yes | Yes | Yes | 12430000.0 | 5.650000e+05 | 22.0 | 99.956841 | 99.927641 |
| 2 | Jim Miller | Southpaw | No | No | No | No | 11741000.0 | 6.522778e+05 | 18.0 | 99.913681 | 99.493488 |
| 3 | Jon Jones | Orthodox | Yes | Yes | Yes | Yes | 10992000.0 | 6.465882e+05 | 17.0 | 99.870522 | 99.204052 |
| 4 | Demian Maia | Southpaw | No | No | Yes | No | 10907000.0 | 5.453500e+05 | 20.0 | 99.827363 | 99.710564 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 133 | Dominick Cruz | Orthodox | Yes | No | Yes | Yes | 3975000.0 | 5.678571e+05 | 7.0 | 94.259819 | 88.205499 |
| 134 | Krzysztof Soszynski | Switch | No | No | No | No | 3935000.0 | 5.621429e+05 | 7.0 | 94.216659 | 88.205499 |
| 135 | Max Griffin | Orthodox | No | No | No | No | 3930000.0 | 9.825000e+05 | 4.0 | 94.173500 | 74.963821 |
| 136 | Derrick Lewis | Orthodox | No | No | Yes | No | 3880000.0 | 7.760000e+05 | 5.0 | 94.130341 | 81.150507 |
| 137 | Aaron Riley | Southpaw | No | No | No | No | 3830000.0 | 6.383333e+05 | 6.0 | 94.087182 | 85.419682 |
138 rows × 11 columns
# Calculate the number of entries that represent the top 1% of the dataset
top_1_percent_count = int(len(fb) * 0.01)
# Sort the dataset by 'Buyrate' descending and take the top 10%
top_1_percent_earners = fb.sort_values(by='Buyrate', ascending=False).head(top_1_percent_count)
top_1_percent_earners
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | Buyrate | Avg_Buyrate_Per_Event | PPV_Fights | Buyrate_PERCENTILE | PPV_Fights_PERCENTILE | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Conor McGregor | Southpaw | Yes | Yes | Yes | Yes | 13555166.0 | 1.355517e+06 | 10.0 | 100.000000 | 94.283647 |
| 1 | Georges St-Pierre | Orthodox | Yes | Yes | Yes | Yes | 12430000.0 | 5.650000e+05 | 22.0 | 99.956841 | 99.927641 |
| 2 | Jim Miller | Southpaw | No | No | No | No | 11741000.0 | 6.522778e+05 | 18.0 | 99.913681 | 99.493488 |
| 3 | Jon Jones | Orthodox | Yes | Yes | Yes | Yes | 10992000.0 | 6.465882e+05 | 17.0 | 99.870522 | 99.204052 |
| 4 | Demian Maia | Southpaw | No | No | Yes | No | 10907000.0 | 5.453500e+05 | 20.0 | 99.827363 | 99.710564 |
| 5 | Anderson Silva | Southpaw | Yes | No | Yes | Yes | 10739000.0 | 5.113810e+05 | 21.0 | 99.784204 | 99.782923 |
| 6 | Johny Hendricks | Southpaw | Yes | No | Yes | Yes | 9924000.0 | 6.616000e+05 | 15.0 | 99.741044 | 98.625181 |
| 7 | Frank Mir | Southpaw | Yes | Yes | Yes | Yes | 9519000.0 | 4.326818e+05 | 22.0 | 99.697885 | 99.927641 |
| 8 | Donald Cerrone | Orthodox | No | No | Yes | No | 9378429.0 | 6.252286e+05 | 15.0 | 99.654726 | 98.625181 |
| 9 | Diego Sanchez | Southpaw | No | No | Yes | No | 9126000.0 | 6.084000e+05 | 15.0 | 99.611567 | 98.625181 |
| 10 | Jeremy Stephens | Orthodox | No | No | No | No | 8625000.0 | 6.160714e+05 | 14.0 | 99.568407 | 98.263386 |
| 11 | Dustin Poirier | Southpaw | No | Yes | Yes | Yes | 8524737.0 | 5.683158e+05 | 15.0 | 99.525248 | 98.335745 |
| 12 | Jon Fitch | Orthodox | No | No | Yes | No | 8519000.0 | 6.085000e+05 | 14.0 | 99.482089 | 97.937771 |
# Filter to top 1% fighters by their buyrate.
top = top_1_percent_earners
# Visualization with vertical bars
plt.figure(figsize=(12, 7))
plt.bar(top['FIGHTER'], top['Buyrate'], color='lightgreen')
plt.xticks(rotation=45, ha='right')
plt.ylabel('Buyrate')
plt.title('Top 1% Fighters by Buyrate')
plt.show()
top_1_percent_list
['Conor McGregor', 'Georges St-Pierre', 'Jim Miller', 'Jon Jones', 'Demian Maia', 'Anderson Silva', 'Johny Hendricks', 'Frank Mir', 'Donald Cerrone', 'Diego Sanchez', 'Jeremy Stephens', 'Dustin Poirier', 'Jon Fitch']
top_10_percent_list
['Conor McGregor', 'Georges St-Pierre', 'Jim Miller', 'Jon Jones', 'Demian Maia', 'Anderson Silva', 'Johny Hendricks', 'Frank Mir', 'Donald Cerrone', 'Diego Sanchez', 'Jeremy Stephens', 'Dustin Poirier', 'Jon Fitch', 'Frankie Edgar', 'Dong Hyun Kim', 'Nate Diaz', 'Thiago Alves', 'Brock Lesnar', 'Michael Bisping', 'Tim Boetsch', 'Khabib Nurmagomedov', 'Gleison Tibau', 'Rashad Evans', 'Lyoto Machida', 'Mark Bocek', 'Forrest Griffin', 'Matt Brown', 'Tito Ortiz', 'Anthony Pettis', 'Gabriel Gonzaga', 'Stefan Struve', 'BJ Penn', 'Chris Weidman', 'John Makdessi', 'Patrick Cote', 'Cody Garbrandt', 'Rick Story', 'Josh Koscheck', 'Matt Hughes', 'Nik Lentz', 'Alan Belcher', 'Stephen Thompson', 'Carlos Condit', 'Cheick Kongo', 'Chris Leben', 'Jose Aldo', 'Tony Ferguson', 'Chuck Liddell', 'Melvin Guillard', 'Amanda Nunes', 'Ronda Rousey', 'Nate Marquardt', 'Yushin Okami', 'Daniel Cormier', 'Stephan Bonnar', 'Cain Velasquez', 'Quinton Jackson', 'Raquel Pennington', 'Sam Stout', 'Max Holloway', 'Tyron Woodley', 'Rich Franklin', 'Jason MacDonald', 'Tom Lawlor', 'Randy Couture', 'Brad Tavares', 'Joe Lauzon', 'Dan Miller', 'Ovince Saint Preux', 'TJ Grant', 'Chris Lytle', 'Miesha Tate', 'Kurt Pellegrino', 'Neil Magny', 'Andrei Arlovski', 'Robbie Lawler', 'Holly Holm', 'Jessica Eye', 'Joe Stevenson', 'Mauricio Rua', 'Chael Sonnen', 'Brendan Schaub', 'Kenny Florian', 'Travis Browne', 'Rose Namajunas', 'Mike Pyle', 'Antonio Rodrigo Nogueira', 'Uriah Hall', 'Martin Kampmann', 'Drew Dober', 'Sergio Pettis', 'Ricardo Almeida', 'Kendall Grove', 'Urijah Faber', 'Thales Leites', 'Gian Villante', 'Edson Barboza', 'John Howard', 'Zhalgas Zhumagulov', 'Tyson Griffin', 'Nick Diaz', 'TJ Dillashaw', 'Junior Dos Santos', 'Darren Elkins', 'Mark Hunt', 'Joanna Jedrzejczyk', 'Matt Hamill', 'Gray Maynard', 'Rafael Dos Anjos', 'Dan Henderson', 'Yana Kunitskaya', 'Mac Danzig', 'CB Dollaway', 'Vicente Luque', 'Clay Guida', 'Anthony Johnson', 'Ed Herman', 'Rory MacDonald', 'Colby Covington', 'Tim Means', 'Manvel Gamburyan', 'Chad Mendes', 'Rafael Natal', 'Paulo Thiago', 'Thiago Santos', 'Luiz Cane', 'Joshua Burkman', 'Evan Dunham', 'George Sotiropoulos', 'Ryan Jensen', 'Jake Ellenberger', 'Omari Akhmedov', 'Keith Jardine', 'Dominick Cruz', 'Krzysztof Soszynski', 'Max Griffin', 'Derrick Lewis', 'Aaron Riley']
# For the top 10% earners, we'll calculate the numbers and percentages for the 'STANCE', 'EVER_CHAMPION', 'EVER_INTERIM', and 'EVER_CHALLENGER' columns
# Filtering to the top 10% earners
top_10_percent = fb_sorted_by_buyrate.head(top_10_percent_count)
# Calculating counts and percentages for each specified column
columns_of_interest = ['STANCE', 'EVER_CHAMPION', 'EVER_INTERIM', 'EVER_CHALLENGER', 'EITHER_CHAMP']
summary = {}
for column in columns_of_interest:
counts = top_10_percent[column].value_counts()
percentages = top_10_percent[column].value_counts(normalize=True) * 100
summary[column] = pd.DataFrame({'Number': counts, 'Percentage': percentages})
summary
{'STANCE': Number Percentage
Orthodox 107 77.536232
Southpaw 28 20.289855
Switch 3 2.173913,
'EVER_CHAMPION': Number Percentage
No 96 69.565217
Yes 42 30.434783,
'EVER_INTERIM': Number Percentage
No 125 90.57971
Yes 13 9.42029,
'EVER_CHALLENGER': Number Percentage
Yes 76 55.072464
No 62 44.927536,
'EITHER_CHAMP': Number Percentage
No 91 65.942029
Yes 47 34.057971}
# For the top 1% earners, we'll calculate the numbers and percentages for the 'STANCE', 'EVER_CHAMPION', 'EVER_INTERIM', and 'EVER_CHALLENGER' columns
# Filtering to the top 1% earners
top_1_percent = fb_sorted_by_buyrate.head(top_1_percent_count)
# Calculating counts and percentages for each specified column
columns_of_interest = ['STANCE', 'EVER_CHAMPION', 'EVER_INTERIM', 'EVER_CHALLENGER', 'EITHER_CHAMP']
summary = {}
for column in columns_of_interest:
counts = top_1_percent[column].value_counts()
percentages = top_1_percent[column].value_counts(normalize=True) * 100
summary[column] = pd.DataFrame({'Number': counts, 'Percentage': percentages})
summary
{'STANCE': Number Percentage
Southpaw 8 61.538462
Orthodox 5 38.461538,
'EVER_CHAMPION': Number Percentage
No 7 53.846154
Yes 6 46.153846,
'EVER_INTERIM': Number Percentage
No 8 61.538462
Yes 5 38.461538,
'EVER_CHALLENGER': Number Percentage
Yes 11 84.615385
No 2 15.384615,
'EITHER_CHAMP': Number Percentage
Yes 7 53.846154
No 6 46.153846}
# Remove rows where either 'STANCE' or 'Buyrate' is missing
cleaned_data = fb.dropna(subset=['STANCE', 'Buyrate'])
# Plotting
plt.figure(figsize=(10, 6))
sns.boxplot(x='STANCE', y='Buyrate', data=cleaned_data)
plt.title('Buyrate Distribution by Stance')
plt.xlabel('Stance')
plt.ylabel('Buyrate')
plt.xticks(rotation=45)
plt.grid(True)
plt.tight_layout()
plt.show()
# Prepare data for ANOVA
stances = cleaned_data['STANCE'].unique()
grouped_data = [cleaned_data['Buyrate'][cleaned_data['STANCE'] == stance] for stance in stances]
# Perform ANOVA
anova_result = f_oneway(*grouped_data)
anova_result
F_onewayResult(statistic=2.0003919810677537, pvalue=0.13567748040766384)
# Perform Tukey HSD test
tukey_result = pairwise_tukeyhsd(endog=cleaned_data['Buyrate'], groups=cleaned_data['STANCE'], alpha=0.05)
# Convert the result to a DataFrame for better readability
tukey_summary = pd.DataFrame(data=tukey_result.summary().data[1:], columns=tukey_result.summary().data[0])
tukey_summary
| group1 | group2 | meandiff | p-adj | lower | upper | reject | |
|---|---|---|---|---|---|---|---|
| 0 | Orthodox | Southpaw | 249202.4272 | 0.1198 | -47447.6301 | 545852.4844 | False |
| 1 | Orthodox | Switch | -37828.4288 | 0.9876 | -626748.4028 | 551091.5451 | False |
| 2 | Southpaw | Switch | -287030.8560 | 0.5373 | -920626.6266 | 346564.9146 | False |
# First, let's convert the 'EITHER_CHAMP' column to a binary format where 'Yes' is 1 and 'No' is 0
fb['EITHER_CHAMP_BINARY'] = (fb['EITHER_CHAMP'] == 'Yes').astype(int)
# Now, let's calculate the correlation between 'EITHER_CHAMP_BINARY' and 'Buyrate'
correlation = fb[['EITHER_CHAMP_BINARY', 'Buyrate']].corr()
correlation
/var/folders/_6/_1qflrx12_j4kc3ssfs46ldw0000gn/T/ipykernel_1785/3371666952.py:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy fb['EITHER_CHAMP_BINARY'] = (fb['EITHER_CHAMP'] == 'Yes').astype(int)
| EITHER_CHAMP_BINARY | Buyrate | |
|---|---|---|
| EITHER_CHAMP_BINARY | 1.000000 | 0.412969 |
| Buyrate | 0.412969 | 1.000000 |
# First, let's convert the 'EITHER_CHAMP' column to a binary format where 'Yes' is 1 and 'No' is 0
fb['EVER_CHALLENGER_BINARY'] = (fb['EVER_CHALLENGER'] == 'Yes').astype(int)
# Now, let's calculate the correlation between 'EITHER_CHAMP_BINARY' and 'Buyrate'
correlation = fb[['EVER_CHALLENGER_BINARY', 'Buyrate']].corr()
correlation
/var/folders/_6/_1qflrx12_j4kc3ssfs46ldw0000gn/T/ipykernel_1785/1082348933.py:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy fb['EVER_CHALLENGER_BINARY'] = (fb['EVER_CHALLENGER'] == 'Yes').astype(int)
| EVER_CHALLENGER_BINARY | Buyrate | |
|---|---|---|
| EVER_CHALLENGER_BINARY | 1.000000 | 0.454714 |
| Buyrate | 0.454714 | 1.000000 |
# Create dummy variables for EITHER_CHAMP and EVER_CHALLENGER columns
fb['EITHER_CHAMP_dummy'] = (fb['EITHER_CHAMP'] == 'Yes').astype(int)
fb['EVER_CHALLENGER_dummy'] = (fb['EVER_CHALLENGER'] == 'Yes').astype(int)
# Create dummy variables for each value in the STANCE column
stance_dummies = pd.get_dummies(fb['STANCE'], prefix='STANCE')
# Combine the dummy variables with the original dataframe
fb_combined = pd.concat([fb, stance_dummies], axis=1)
# Display the columns of the modified dataframe to ensure the dummies were added correctly
# fb_combined.columns.tolist()
# Correcting the mistake to include STANCE dummy variables in the correlation matrix
full_correlation_matrix = fb_combined[['Buyrate', 'Avg_Buyrate_Per_Event', 'PPV_Fights',
'EITHER_CHAMP_dummy', 'EVER_CHALLENGER_dummy',
'STANCE_Orthodox', 'STANCE_Southpaw', 'STANCE_Switch']].corr()
full_correlation_matrix
/var/folders/_6/_1qflrx12_j4kc3ssfs46ldw0000gn/T/ipykernel_1785/3780777172.py:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy fb['EITHER_CHAMP_dummy'] = (fb['EITHER_CHAMP'] == 'Yes').astype(int) /var/folders/_6/_1qflrx12_j4kc3ssfs46ldw0000gn/T/ipykernel_1785/3780777172.py:3: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy fb['EVER_CHALLENGER_dummy'] = (fb['EVER_CHALLENGER'] == 'Yes').astype(int)
| Buyrate | Avg_Buyrate_Per_Event | PPV_Fights | EITHER_CHAMP_dummy | EVER_CHALLENGER_dummy | STANCE_Orthodox | STANCE_Southpaw | STANCE_Switch | |
|---|---|---|---|---|---|---|---|---|
| Buyrate | 1.000000 | 0.314592 | 0.894954 | 0.412969 | 0.454714 | -0.036963 | 0.055777 | -0.008288 |
| Avg_Buyrate_Per_Event | 0.314592 | 1.000000 | 0.000047 | 0.028900 | 0.020710 | -0.042655 | 0.020872 | 0.050279 |
| PPV_Fights | 0.894954 | 0.000047 | 1.000000 | 0.457227 | 0.508737 | -0.028501 | 0.055813 | -0.024089 |
| EITHER_CHAMP_dummy | 0.412969 | 0.028900 | 0.457227 | 1.000000 | 0.659646 | -0.020250 | 0.005268 | 0.043811 |
| EVER_CHALLENGER_dummy | 0.454714 | 0.020710 | 0.508737 | 0.659646 | 1.000000 | -0.032738 | 0.024254 | 0.038510 |
| STANCE_Orthodox | -0.036963 | -0.042655 | -0.028501 | -0.020250 | -0.032738 | 1.000000 | -0.866698 | -0.371894 |
| STANCE_Southpaw | 0.055777 | 0.020872 | 0.055813 | 0.005268 | 0.024254 | -0.866698 | 1.000000 | -0.094764 |
| STANCE_Switch | -0.008288 | 0.050279 | -0.024089 | 0.043811 | 0.038510 | -0.371894 | -0.094764 | 1.000000 |
# Create dummy variables for EITHER_CHAMP and EVER_CHALLENGER columns
top_10_percent['EITHER_CHAMP_dummy'] = (top_10_percent['EITHER_CHAMP'] == 'Yes').astype(int)
top_10_percent['EVER_CHALLENGER_dummy'] = (top_10_percent['EVER_CHALLENGER'] == 'Yes').astype(int)
# Create dummy variables for each value in the STANCE column
stance_dummies = pd.get_dummies(top_10_percent['STANCE'], prefix='STANCE')
# Combine the dummy variables with the original dataframe
fb_combined = pd.concat([top_10_percent, stance_dummies], axis=1)
# Display the columns of the modified dataframe to ensure the dummies were added correctly
# fb_combined.columns.tolist()
# Correcting the mistake to include STANCE dummy variables in the correlation matrix
full_correlation_matrix = fb_combined[['Buyrate', 'Avg_Buyrate_Per_Event', 'PPV_Fights',
'EITHER_CHAMP_dummy', 'EVER_CHALLENGER_dummy',
'STANCE_Orthodox', 'STANCE_Southpaw', 'STANCE_Switch']].corr()
full_correlation_matrix
/var/folders/_6/_1qflrx12_j4kc3ssfs46ldw0000gn/T/ipykernel_1785/2837436102.py:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy top_10_percent['EITHER_CHAMP_dummy'] = (top_10_percent['EITHER_CHAMP'] == 'Yes').astype(int) /var/folders/_6/_1qflrx12_j4kc3ssfs46ldw0000gn/T/ipykernel_1785/2837436102.py:3: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy top_10_percent['EVER_CHALLENGER_dummy'] = (top_10_percent['EVER_CHALLENGER'] == 'Yes').astype(int)
| Buyrate | Avg_Buyrate_Per_Event | PPV_Fights | EITHER_CHAMP_dummy | EVER_CHALLENGER_dummy | STANCE_Orthodox | STANCE_Southpaw | STANCE_Switch | |
|---|---|---|---|---|---|---|---|---|
| Buyrate | 1.000000 | 0.049096 | 0.685557 | 0.308537 | 0.292038 | -0.268087 | 0.302463 | -0.066884 |
| Avg_Buyrate_Per_Event | 0.049096 | 1.000000 | -0.584288 | -0.097268 | -0.137815 | -0.024369 | -0.027014 | 0.144238 |
| PPV_Fights | 0.685557 | -0.584288 | 1.000000 | 0.366633 | 0.362326 | -0.224168 | 0.233117 | -0.001336 |
| EITHER_CHAMP_dummy | 0.308537 | -0.097268 | 0.366633 | 1.000000 | 0.649108 | -0.016195 | 0.017633 | -0.002279 |
| EVER_CHALLENGER_dummy | 0.292038 | -0.137815 | 0.362326 | 0.649108 | 1.000000 | -0.067283 | 0.093448 | -0.065150 |
| STANCE_Orthodox | -0.268087 | -0.024369 | -0.224168 | -0.016195 | -0.067283 | 1.000000 | -0.937333 | -0.276952 |
| STANCE_Southpaw | 0.302463 | -0.027014 | 0.233117 | 0.017633 | 0.093448 | -0.937333 | 1.000000 | -0.075210 |
| STANCE_Switch | -0.066884 | 0.144238 | -0.001336 | -0.002279 | -0.065150 | -0.276952 | -0.075210 | 1.000000 |
# Create dummy variables for EITHER_CHAMP and EVER_CHALLENGER columns
top_1_percent['EITHER_CHAMP_dummy'] = (top_1_percent['EITHER_CHAMP'] == 'Yes').astype(int)
top_1_percent['EVER_CHALLENGER_dummy'] = (top_1_percent['EVER_CHALLENGER'] == 'Yes').astype(int)
# Create dummy variables for each value in the STANCE column
stance_dummies = pd.get_dummies(top_1_percent['STANCE'], prefix='STANCE')
# Combine the dummy variables with the original dataframe
fb_combined = pd.concat([top_1_percent, stance_dummies], axis=1)
# Display the columns of the modified dataframe to ensure the dummies were added correctly
# fb_combined.columns.tolist()
# Correcting the mistake to include STANCE dummy variables in the correlation matrix
full_correlation_matrix = fb_combined[['Buyrate', 'Avg_Buyrate_Per_Event', 'PPV_Fights',
'EITHER_CHAMP_dummy', 'EVER_CHALLENGER_dummy',
'STANCE_Orthodox', 'STANCE_Southpaw']].corr()
full_correlation_matrix
/var/folders/_6/_1qflrx12_j4kc3ssfs46ldw0000gn/T/ipykernel_1785/860985673.py:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy top_1_percent['EITHER_CHAMP_dummy'] = (top_1_percent['EITHER_CHAMP'] == 'Yes').astype(int) /var/folders/_6/_1qflrx12_j4kc3ssfs46ldw0000gn/T/ipykernel_1785/860985673.py:3: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy top_1_percent['EVER_CHALLENGER_dummy'] = (top_1_percent['EVER_CHALLENGER'] == 'Yes').astype(int)
| Buyrate | Avg_Buyrate_Per_Event | PPV_Fights | EITHER_CHAMP_dummy | EVER_CHALLENGER_dummy | STANCE_Orthodox | STANCE_Southpaw | |
|---|---|---|---|---|---|---|---|
| Buyrate | 1.000000 | 0.589853 | 0.113339 | 0.357520 | 0.034371 | -0.164151 | 0.164151 |
| Avg_Buyrate_Per_Event | 0.589853 | 1.000000 | -0.704153 | 0.158545 | 0.023418 | -0.124390 | 0.124390 |
| PPV_Fights | 0.113339 | -0.704153 | 1.000000 | 0.204082 | 0.093993 | -0.083649 | 0.083649 |
| EITHER_CHAMP_dummy | 0.357520 | 0.158545 | 0.204082 | 1.000000 | 0.460566 | -0.219578 | 0.219578 |
| EVER_CHALLENGER_dummy | 0.034371 | 0.023418 | 0.093993 | 0.460566 | 1.000000 | -0.101130 | 0.101130 |
| STANCE_Orthodox | -0.164151 | -0.124390 | -0.083649 | -0.219578 | -0.101130 | 1.000000 | -1.000000 |
| STANCE_Southpaw | 0.164151 | 0.124390 | 0.083649 | 0.219578 | 0.101130 | -1.000000 | 1.000000 |
# Display max rows
pd.set_option('display.max_rows', None)
breakdown(frame)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)
Distinct Count FIGHTER 2317 STANCE 3 EVER_CHAMPION 2 EVER_INTERIM 2 EVER_CHALLENGER 2 EITHER_CHAMP 2 HEIGHT 23 WEIGHT 48 REACH 27 APE_INDEX 179 FRAME 44 HEIGHT_DIFF 772 REACH_DIFF 809 AGE_DIFF 1583 AI_DIFF 1078 FRAME_DIFF 906 OPP_HEIGHT 569 OPP_WEIGHT 618 OPP_REACH 592 OPP_APE_INDEX 1829 OPP_FRAME 748 OPP_REACH_DIFF 1431 OPP_AGE_DIFF 1597 OPP_AI_DIFF 2052 OPP_FRAME_DIFF 1534 HEIGHT_DELTA 467 REACH_DELTA 518 AGE_DELTA 610 AI_DELTA 1916 FRAME_DELTA 620 HEIGHT_DELTA_RANK 467 REACH_DELTA_RANK 518 AGE_DELTA_RANK 610 AI_DELTA_RANK 1916 FRAME_DELTA_RANK 620 HEIGHT_DELTA_PERCENTILE 467 REACH_DELTA_PERCENTILE 518 FRAME_DELTA_PERCENTILE 620 dtype: int64 Count FIGHTER 2317 STANCE 2075 EVER_CHAMPION 2317 EVER_INTERIM 2317 EVER_CHALLENGER 2317 EITHER_CHAMP 2317 HEIGHT 2180 WEIGHT 2285 REACH 2180 APE_INDEX 2180 FRAME 2180 HEIGHT_DIFF 2180 REACH_DIFF 2180 AGE_DIFF 2221 AI_DIFF 2180 FRAME_DIFF 2180 OPP_HEIGHT 2278 OPP_WEIGHT 2310 OPP_REACH 2278 OPP_APE_INDEX 2278 OPP_FRAME 2278 OPP_REACH_DIFF 2278 OPP_AGE_DIFF 2297 OPP_AI_DIFF 2278 OPP_FRAME_DIFF 2278 HEIGHT_DELTA 2173 REACH_DELTA 2173 AGE_DELTA 2206 AI_DELTA 2173 FRAME_DELTA 2173 HEIGHT_DELTA_RANK 2173 REACH_DELTA_RANK 2173 AGE_DELTA_RANK 2206 AI_DELTA_RANK 2173 FRAME_DELTA_RANK 2173 HEIGHT_DELTA_PERCENTILE 2173 REACH_DELTA_PERCENTILE 2173 FRAME_DELTA_PERCENTILE 2173 dtype: int64 DTypes FIGHTER object STANCE object EVER_CHAMPION object EVER_INTERIM object EVER_CHALLENGER object EITHER_CHAMP object HEIGHT float64 WEIGHT float64 REACH float64 APE_INDEX float64 FRAME float64 HEIGHT_DIFF float64 REACH_DIFF float64 AGE_DIFF float64 AI_DIFF float64 FRAME_DIFF float64 OPP_HEIGHT float64 OPP_WEIGHT float64 OPP_REACH float64 OPP_APE_INDEX float64 OPP_FRAME float64 OPP_REACH_DIFF float64 OPP_AGE_DIFF float64 OPP_AI_DIFF float64 OPP_FRAME_DIFF float64 HEIGHT_DELTA float64 REACH_DELTA float64 AGE_DELTA float64 AI_DELTA float64 FRAME_DELTA float64 HEIGHT_DELTA_RANK float64 REACH_DELTA_RANK float64 AGE_DELTA_RANK float64 AI_DELTA_RANK float64 FRAME_DELTA_RANK float64 HEIGHT_DELTA_PERCENTILE float64 REACH_DELTA_PERCENTILE float64 FRAME_DELTA_PERCENTILE float64 dtype: object Shape (2317, 38) Nulls FIGHTER 0 STANCE 242 EVER_CHAMPION 0 EVER_INTERIM 0 EVER_CHALLENGER 0 EITHER_CHAMP 0 HEIGHT 137 WEIGHT 32 REACH 137 APE_INDEX 137 FRAME 137 HEIGHT_DIFF 137 REACH_DIFF 137 AGE_DIFF 96 AI_DIFF 137 FRAME_DIFF 137 OPP_HEIGHT 39 OPP_WEIGHT 7 OPP_REACH 39 OPP_APE_INDEX 39 OPP_FRAME 39 OPP_REACH_DIFF 39 OPP_AGE_DIFF 20 OPP_AI_DIFF 39 OPP_FRAME_DIFF 39 HEIGHT_DELTA 144 REACH_DELTA 144 AGE_DELTA 111 AI_DELTA 144 FRAME_DELTA 144 HEIGHT_DELTA_RANK 144 REACH_DELTA_RANK 144 AGE_DELTA_RANK 111 AI_DELTA_RANK 144 FRAME_DELTA_RANK 144 HEIGHT_DELTA_PERCENTILE 144 REACH_DELTA_PERCENTILE 144 FRAME_DELTA_PERCENTILE 144 dtype: int64 Null % FIGHTER 0.00 STANCE 10.44 EVER_CHAMPION 0.00 EVER_INTERIM 0.00 EVER_CHALLENGER 0.00 EITHER_CHAMP 0.00 HEIGHT 5.91 WEIGHT 1.38 REACH 5.91 APE_INDEX 5.91 FRAME 5.91 HEIGHT_DIFF 5.91 REACH_DIFF 5.91 AGE_DIFF 4.14 AI_DIFF 5.91 FRAME_DIFF 5.91 OPP_HEIGHT 1.68 OPP_WEIGHT 0.30 OPP_REACH 1.68 OPP_APE_INDEX 1.68 OPP_FRAME 1.68 OPP_REACH_DIFF 1.68 OPP_AGE_DIFF 0.86 OPP_AI_DIFF 1.68 OPP_FRAME_DIFF 1.68 HEIGHT_DELTA 6.21 REACH_DELTA 6.21 AGE_DELTA 4.79 AI_DELTA 6.21 FRAME_DELTA 6.21 HEIGHT_DELTA_RANK 6.21 REACH_DELTA_RANK 6.21 AGE_DELTA_RANK 4.79 AI_DELTA_RANK 6.21 FRAME_DELTA_RANK 6.21 HEIGHT_DELTA_PERCENTILE 6.21 REACH_DELTA_PERCENTILE 6.21 FRAME_DELTA_PERCENTILE 6.21 dtype: float64
frame.describe()
| HEIGHT | WEIGHT | REACH | APE_INDEX | FRAME | HEIGHT_DIFF | REACH_DIFF | AGE_DIFF | AI_DIFF | FRAME_DIFF | OPP_HEIGHT | OPP_WEIGHT | OPP_REACH | OPP_APE_INDEX | OPP_FRAME | OPP_REACH_DIFF | OPP_AGE_DIFF | OPP_AI_DIFF | OPP_FRAME_DIFF | HEIGHT_DELTA | REACH_DELTA | AGE_DELTA | AI_DELTA | FRAME_DELTA | HEIGHT_DELTA_RANK | REACH_DELTA_RANK | AGE_DELTA_RANK | AI_DELTA_RANK | FRAME_DELTA_RANK | HEIGHT_DELTA_PERCENTILE | REACH_DELTA_PERCENTILE | FRAME_DELTA_PERCENTILE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 2180.000000 | 2285.000000 | 2180.000000 | 2180.000000 | 2180.000000 | 2180.000000 | 2180.000000 | 2221.000000 | 2180.000000 | 2180.000000 | 2278.000000 | 2310.000000 | 2278.000000 | 2278.000000 | 2278.000000 | 2278.000000 | 2297.000000 | 2278.000000 | 2278.000000 | 2173.000000 | 2173.000000 | 2206.000000 | 2173.000000 | 2173.000000 | 2173.000000 | 2173.000000 | 2206.000000 | 2173.000000 | 2173.000000 | 2173.000000 | 2173.000000 | 2173.000000 |
| mean | 70.160092 | 166.136543 | 71.490826 | 1.018953 | 70.825459 | -0.058956 | -0.466490 | -0.564307 | -0.005690 | -0.262723 | 70.193045 | 165.988108 | 71.830934 | 1.023263 | 71.011989 | -0.056961 | -0.636821 | -0.001250 | -0.011852 | -0.083528 | -0.399578 | 0.056264 | -0.004412 | -0.241553 | 241.616659 | 285.902899 | 303.892112 | 986.327197 | 332.572941 | 50.023010 | 50.023010 | 50.023010 |
| std | 3.482015 | 34.890644 | 4.011389 | 0.026378 | 3.640943 | 1.942093 | 2.564479 | 3.548923 | 0.026326 | 2.080214 | 3.148040 | 34.201223 | 3.642067 | 0.017115 | 3.350022 | 1.563383 | 2.566301 | 0.016283 | 1.252395 | 2.077244 | 2.734528 | 4.109363 | 0.029627 | 2.198091 | 124.078774 | 140.424174 | 165.529317 | 542.493323 | 166.551398 | 28.864216 | 28.867396 | 28.871629 |
| min | 60.000000 | 115.000000 | 58.000000 | 0.920635 | 60.000000 | -9.328423 | -11.251590 | -11.267538 | -0.082762 | -10.290007 | 60.000000 | 115.000000 | 60.000000 | 0.946667 | 60.000000 | -7.251590 | -11.267538 | -0.082565 | -5.665245 | -7.000000 | -13.000000 | -13.000000 | -0.132219 | -8.500000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.115048 | 0.046019 | 0.069029 |
| 25% | 68.000000 | 145.000000 | 69.000000 | 1.000000 | 68.500000 | -1.328423 | -2.185220 | -3.101245 | -0.027023 | -1.592139 | 68.000000 | 140.000000 | 69.381250 | 1.013699 | 68.666667 | -0.875164 | -2.125546 | -0.010501 | -0.671204 | -1.500000 | -2.000000 | -2.980769 | -0.024443 | -1.600000 | 141.000000 | 173.000000 | 166.000000 | 539.000000 | 198.000000 | 23.815002 | 27.105384 | 24.988495 |
| 50% | 70.000000 | 155.000000 | 72.000000 | 1.013889 | 71.000000 | -0.088424 | -0.501138 | -0.685643 | -0.010878 | -0.290007 | 70.285714 | 158.215909 | 72.000000 | 1.023611 | 71.166667 | 0.013388 | -0.434578 | -0.001392 | 0.036262 | 0.000000 | -0.333333 | 0.000000 | -0.005616 | -0.208333 | 232.000000 | 287.000000 | 301.000000 | 976.000000 | 338.000000 | 52.600092 | 50.253106 | 50.046019 |
| 75% | 73.000000 | 185.000000 | 74.000000 | 1.039089 | 73.500000 | 1.170691 | 1.217421 | 1.897596 | 0.012026 | 1.108479 | 72.567460 | 185.000000 | 74.500000 | 1.032544 | 73.500000 | 0.765050 | 0.950849 | 0.007051 | 0.709993 | 1.166667 | 1.333333 | 2.700000 | 0.015002 | 1.131579 | 350.000000 | 398.000000 | 451.750000 | 1462.000000 | 476.000000 | 74.873447 | 74.666360 | 75.011505 |
| max | 83.000000 | 275.000000 | 84.000000 | 1.125000 | 83.500000 | 8.138963 | 8.720020 | 13.353224 | 0.101657 | 7.589222 | 79.500000 | 265.000000 | 82.000000 | 1.109375 | 80.250000 | 7.748410 | 15.172323 | 0.084828 | 5.709993 | 8.083333 | 10.333333 | 16.000000 | 0.106059 | 8.333333 | 467.000000 | 518.000000 | 610.000000 | 1916.000000 | 620.000000 | 100.000000 | 100.000000 | 100.000000 |
subset_columns = [
'FIGHTER', 'STANCE', 'EVER_CHAMPION', 'EVER_INTERIM', 'EVER_CHALLENGER', 'EITHER_CHAMP',
'APE_INDEX', 'HEIGHT_DELTA', 'REACH_DELTA', 'FRAME_DELTA', 'HEIGHT_DELTA_PERCENTILE',
'REACH_DELTA_PERCENTILE', 'FRAME_DELTA_PERCENTILE'
]
# Creating the subset dataframe
subset_df = frame[subset_columns]
frame_champions = subset_df[subset_df['EITHER_CHAMP'] == 'Yes']
frame_champions
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | APE_INDEX | HEIGHT_DELTA | REACH_DELTA | FRAME_DELTA | HEIGHT_DELTA_PERCENTILE | REACH_DELTA_PERCENTILE | FRAME_DELTA_PERCENTILE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5 | Dustin Poirier | Southpaw | No | Yes | Yes | Yes | 1.043478 | -0.733333 | 0.366667 | -0.183333 | 37.689830 | 61.297745 | 50.529222 |
| 17 | Rafael Dos Anjos | Southpaw | Yes | No | Yes | Yes | 1.029412 | -2.428571 | -2.371429 | -2.400000 | 11.803958 | 22.411413 | 15.485504 |
| 22 | Petr Yan | Switch | Yes | Yes | Yes | Yes | 1.000000 | 0.076923 | -1.923077 | -0.923077 | 55.384261 | 29.498389 | 37.735849 |
| 27 | Sean O'Malley | Switch | Yes | No | Yes | Yes | 1.014085 | 3.500000 | 2.916667 | 3.208333 | 96.042338 | 88.748274 | 94.845835 |
| 50 | Brandon Moreno | Orthodox | Yes | Yes | Yes | Yes | 1.044776 | 1.200000 | 2.266667 | 1.733333 | 75.218592 | 84.583525 | 82.236539 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2241 | Carlos Newton | Orthodox | Yes | No | Yes | Yes | 1.000000 | -1.250000 | -2.250000 | -1.750000 | 27.979751 | 23.676944 | 23.101703 |
| 2246 | Ricco Rodriguez | Orthodox | Yes | No | Yes | Yes | 1.000000 | 2.285714 | 1.000000 | 1.642857 | 87.735849 | 70.432582 | 81.132075 |
| 2273 | Murilo Bustamante | Orthodox | Yes | No | Yes | Yes | 1.000000 | 2.000000 | 0.666667 | 1.333333 | 84.836631 | 66.037736 | 77.657616 |
| 2278 | Pat Miletich | Orthodox | Yes | No | Yes | Yes | 1.000000 | -0.600000 | -1.400000 | -1.000000 | 40.128854 | 35.066728 | 35.687989 |
| 2282 | Kevin Randleman | Orthodox | Yes | No | Yes | Yes | 1.000000 | -3.250000 | -4.750000 | -4.000000 | 5.338242 | 6.189600 | 4.555913 |
105 rows × 13 columns
frame_champions.describe()
| APE_INDEX | HEIGHT_DELTA | REACH_DELTA | FRAME_DELTA | HEIGHT_DELTA_PERCENTILE | REACH_DELTA_PERCENTILE | FRAME_DELTA_PERCENTILE | |
|---|---|---|---|---|---|---|---|
| count | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 |
| mean | 1.026797 | 0.056519 | 0.200751 | 0.128635 | 52.694103 | 56.344093 | 55.304933 |
| std | 0.027708 | 1.694103 | 2.395933 | 1.831165 | 25.876933 | 26.727779 | 26.141637 |
| min | 0.955882 | -4.000000 | -6.400000 | -5.033333 | 2.922227 | 1.679705 | 1.702715 |
| 25% | 1.000000 | -0.812500 | -1.400000 | -1.000000 | 36.493327 | 35.066728 | 35.687989 |
| 50% | 1.027027 | 0.000000 | 0.235294 | 0.095238 | 52.600092 | 58.950759 | 55.729406 |
| 75% | 1.044776 | 1.166667 | 1.571429 | 1.500000 | 74.873447 | 77.542568 | 79.475380 |
| max | 1.105263 | 5.500000 | 8.260870 | 5.173913 | 99.332720 | 99.907961 | 99.171652 |
frame_challengers = subset_df[subset_df['EVER_CHALLENGER'] == 'Yes']
frame_challengers
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | APE_INDEX | HEIGHT_DELTA | REACH_DELTA | FRAME_DELTA | HEIGHT_DELTA_PERCENTILE | REACH_DELTA_PERCENTILE | FRAME_DELTA_PERCENTILE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5 | Dustin Poirier | Southpaw | No | Yes | Yes | Yes | 1.043478 | -0.733333 | 0.366667 | -0.183333 | 37.689830 | 61.297745 | 50.529222 |
| 6 | Gilbert Burns | Orthodox | No | No | Yes | No | 1.014286 | -0.818182 | -1.727273 | -1.272727 | 36.424298 | 31.201104 | 29.797515 |
| 17 | Rafael Dos Anjos | Southpaw | Yes | No | Yes | Yes | 1.029412 | -2.428571 | -2.371429 | -2.400000 | 11.803958 | 22.411413 | 15.485504 |
| 22 | Petr Yan | Switch | Yes | Yes | Yes | Yes | 1.000000 | 0.076923 | -1.923077 | -0.923077 | 55.384261 | 29.498389 | 37.735849 |
| 26 | Marlon Vera | Switch | No | No | Yes | No | 1.029412 | 0.521739 | 1.347826 | 0.934783 | 63.368615 | 75.103543 | 71.237920 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2277 | Hayato Sakurai | Orthodox | No | No | Yes | No | 1.000000 | -2.000000 | -6.000000 | -4.000000 | 16.820064 | 2.439024 | 4.555913 |
| 2278 | Pat Miletich | Orthodox | Yes | No | Yes | Yes | 1.000000 | -0.600000 | -1.400000 | -1.000000 | 40.128854 | 35.066728 | 35.687989 |
| 2282 | Kevin Randleman | Orthodox | Yes | No | Yes | Yes | 1.000000 | -3.250000 | -4.750000 | -4.000000 | 5.338242 | 6.189600 | 4.555913 |
| 2289 | Yuki Kondo | Southpaw | No | No | Yes | No | 1.000000 | -2.666667 | -3.000000 | -2.833333 | 9.710078 | 16.175794 | 11.527842 |
| 2296 | Kenichi Yamamoto | Orthodox | No | No | Yes | No | 1.000000 | 2.000000 | 2.000000 | 2.000000 | 84.836631 | 81.845375 | 86.102163 |
221 rows × 13 columns
frame_challengers.describe()
| APE_INDEX | HEIGHT_DELTA | REACH_DELTA | FRAME_DELTA | HEIGHT_DELTA_PERCENTILE | REACH_DELTA_PERCENTILE | FRAME_DELTA_PERCENTILE | |
|---|---|---|---|---|---|---|---|
| count | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 |
| mean | 1.025508 | -0.033799 | 0.107765 | 0.036983 | 50.925176 | 55.299094 | 53.820125 |
| std | 0.027757 | 1.797022 | 2.417903 | 1.889372 | 26.200253 | 26.819117 | 26.540393 |
| min | 0.955882 | -6.571429 | -7.388889 | -6.583333 | 0.230097 | 0.690290 | 0.460193 |
| 25% | 1.000000 | -1.076923 | -1.500000 | -1.071429 | 29.682467 | 33.548090 | 33.617119 |
| 50% | 1.027027 | -0.041667 | 0.000000 | 0.000000 | 49.930971 | 55.453290 | 53.796595 |
| 75% | 1.043478 | 1.000000 | 1.571429 | 1.312500 | 71.514036 | 77.542568 | 77.450529 |
| max | 1.120000 | 6.500000 | 8.260870 | 6.125000 | 99.769903 | 99.907961 | 99.654855 |
frame_non_champions = subset_df[subset_df['EITHER_CHAMP'] != 'Yes']
frame_non_champions.describe()
| APE_INDEX | HEIGHT_DELTA | REACH_DELTA | FRAME_DELTA | HEIGHT_DELTA_PERCENTILE | REACH_DELTA_PERCENTILE | FRAME_DELTA_PERCENTILE | |
|---|---|---|---|---|---|---|---|
| count | 2075.000000 | 2068.000000 | 2068.000000 | 2068.000000 | 2068.000000 | 2068.000000 | 2068.000000 |
| mean | 1.018556 | -0.090638 | -0.430059 | -0.260349 | 49.887388 | 49.702065 | 49.754827 |
| std | 0.026253 | 2.094919 | 2.747623 | 2.213824 | 29.006774 | 28.941008 | 28.983471 |
| min | 0.920635 | -7.000000 | -13.000000 | -8.500000 | 0.115048 | 0.046019 | 0.069029 |
| 25% | 1.000000 | -1.500000 | -2.090909 | -1.625000 | 23.815002 | 24.643350 | 24.551312 |
| 50% | 1.013889 | -0.064583 | -0.375000 | -0.228220 | 49.804418 | 49.654855 | 49.689370 |
| 75% | 1.031746 | 1.166667 | 1.333333 | 1.125000 | 74.873447 | 74.666360 | 74.827428 |
| max | 1.125000 | 8.083333 | 10.333333 | 8.333333 | 100.000000 | 100.000000 | 100.000000 |
frame_non_challengers = subset_df[subset_df['EVER_CHALLENGER'] != 'Yes']
frame_non_challengers.describe()
| APE_INDEX | HEIGHT_DELTA | REACH_DELTA | FRAME_DELTA | HEIGHT_DELTA_PERCENTILE | REACH_DELTA_PERCENTILE | FRAME_DELTA_PERCENTILE | |
|---|---|---|---|---|---|---|---|
| count | 1959.000000 | 1952.000000 | 1952.000000 | 1952.000000 | 1952.000000 | 1952.000000 | 1952.000000 |
| mean | 1.018213 | -0.089158 | -0.457019 | -0.273088 | 49.920869 | 49.425666 | 49.593111 |
| std | 0.026122 | 2.106956 | 2.762785 | 2.228585 | 29.154905 | 29.036276 | 29.098911 |
| min | 0.920635 | -7.000000 | -13.000000 | -8.500000 | 0.115048 | 0.046019 | 0.069029 |
| 25% | 1.000000 | -1.500000 | -2.135714 | -1.666667 | 23.815002 | 24.275196 | 24.160147 |
| 50% | 1.013699 | 0.000000 | -0.428571 | -0.240385 | 52.600092 | 49.148642 | 49.378739 |
| 75% | 1.031250 | 1.203571 | 1.333333 | 1.114583 | 75.247354 | 74.666360 | 74.689370 |
| max | 1.125000 | 8.083333 | 10.333333 | 8.333333 | 100.000000 | 100.000000 | 100.000000 |
# Plotting the histogram of APE_INDEX for fighters who have been a champion
plt.figure(figsize=(10, 6))
plt.hist(frame_champions['APE_INDEX'].dropna(), bins=20, color='gold', edgecolor='black')
plt.title('APE_Index of Champions')
plt.xlabel('APE_INDEX')
plt.ylabel('Frequency')
plt.grid(axis='y', alpha=0.75)
# Show the plot
plt.show()
# Separate APE_INDEX based on EITHER_CHAMP 'Yes' or 'No'
ape_yes = frame[frame['EITHER_CHAMP'] == 'Yes']['APE_INDEX']
ape_no = frame[frame['EITHER_CHAMP'] == 'No']['APE_INDEX']
# Plotting the histogram with two colors
plt.figure(figsize=(10, 6))
plt.hist([ape_yes.dropna(), ape_no.dropna()], bins=20, color=['gold', 'silver'], label=['Champion', 'Non-Champion'], edgecolor='black')
plt.title('Histogram of APE_INDEX for All Fighters')
plt.xlabel('APE_INDEX')
plt.ylabel('Frequency')
plt.legend()
plt.grid(axis='y', alpha=0.75)
# Show the plot
plt.show()
# Plotting the histogram of FRAME_DELTA for fighters who have been a champion
plt.figure(figsize=(10, 6))
plt.hist(frame_champions['FRAME_DELTA'].dropna(), bins=20, color='chocolate', edgecolor='black')
plt.title('FRAME_DELTA of Champions')
plt.xlabel('FRAME_DELTA')
plt.ylabel('Frequency')
plt.grid(axis='y', alpha=0.75)
# Show the plot
plt.show()
# Separate APE_INDEX based on EITHER_CHAMP 'Yes' or 'No'
h_yes = frame[frame['EITHER_CHAMP'] == 'Yes']['FRAME_DELTA']
h_no = frame[frame['EITHER_CHAMP'] == 'No']['FRAME_DELTA']
# Plotting the histogram with two colors
plt.figure(figsize=(10, 6))
plt.hist([h_yes.dropna(), h_no.dropna()], bins=20, color=['chocolate', 'lawngreen'], label=['Champion', 'Non-Champion'], edgecolor='black')
plt.title('Histogram of FRAME_DELTA for All Fighters')
plt.xlabel('FRAME_DELTA')
plt.ylabel('Frequency')
plt.legend()
plt.grid(axis='y', alpha=0.75)
# Show the plot
plt.show()
# Filter the dataframe using the correct column name "FIGHTER"
c = career[career['FIGHTER'].isin(fighters_list)]
c
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | KD | TD | SUB.ATT | REV | CTRL | HEAD | BODY | LEG | DISTANCE | CLINCH | GROUND | SIG | TOT.SIG | STR | TOTAL.STR | TOTAL.TD | TOTAL.HEAD | TOTAL.BODY | TOTAL.LEG | TOTAL.DISTANCE | TOTAL.CLINCH | TOTAL.GROUND | OPP_KD | OPP_TD | OPP_SUB.ATT | OPP_REV | OPP_CTRL | OPP_HEAD | OPP_BODY | OPP_LEG | OPP_DISTANCE | OPP_CLINCH | OPP_GROUND | OPP_SIG | OPP_TOT.SIG | OPP_STR | OPP_TOTAL.STR | OPP_TOTAL.TD | OPP_TOTAL.HEAD | OPP_TOTAL.BODY | OPP_TOTAL.LEG | OPP_TOTAL.DISTANCE | OPP_TOTAL.CLINCH | OPP_TOTAL.GROUND | TD_DEF | HEAD_DEF | BODY_DEF | LEG_DEF | DISTANCE_DEF | CLINCH_DEF | GROUND_DEF | SIG_DEF | STR_DEF | TD_% | HEAD_% | BODY_% | LEG_% | DISTANCE_% | CLINCH_% | GROUND_% | SIG_% | STR_% | TD_DEF_% | HEAD_DEF_% | BODY_DEF_% | LEG_DEF_% | DISTANCE_DEF_% | CLINCH_DEF_% | GROUND_DEF_% | SIG_DEF_% | STR_DEF_% | KD_PERCENTILE | TD_PERCENTILE | SUB.ATT_PERCENTILE | REV_PERCENTILE | CTRL_PERCENTILE | HEAD_PERCENTILE | BODY_PERCENTILE | LEG_PERCENTILE | DISTANCE_PERCENTILE | CLINCH_PERCENTILE | GROUND_PERCENTILE | SIG_PERCENTILE | STR_PERCENTILE | STR_%_PERCENTILE | TD_DEF_PERCENTILE | HEAD_DEF_PERCENTILE | BODY_DEF_PERCENTILE | LEG_DEF_PERCENTILE | DISTANCE_DEF_PERCENTILE | CLINCH_DEF_PERCENTILE | GROUND_DEF_PERCENTILE | SIG_DEF_PERCENTILE | STR_DEF_PERCENTILE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | CJ Vergara | Orthodox | No | No | No | No | 0.0 | 0.0 | 1.0 | 1.0 | 10.750000 | 203.0 | 149.0 | 52.0 | 285.0 | 35.0 | 84.0 | 404.0 | 700.0 | 579.0 | 895.0 | 0.0 | 448.0 | 193.0 | 59.0 | 531.0 | 46.0 | 123.0 | 2.0 | 16.0 | 5.0 | 2.0 | 20.200000 | 222.0 | 91.0 | 49.0 | 314.0 | 24.0 | 24.0 | 362.0 | 762.0 | 438.0 | 858.0 | 40.0 | 588.0 | 121.0 | 53.0 | 699.0 | 32.0 | 31.0 | 24.0 | 366.0 | 30.0 | 4.0 | 385.0 | 8.0 | 7.0 | 400.0 | 420.0 | NaN | 0.453125 | 0.772021 | 0.881356 | 0.536723 | 0.760870 | 0.682927 | 0.577143 | 0.646927 | 0.600000 | 0.622449 | 0.247934 | 0.075472 | 0.550787 | 0.250000 | 0.225806 | 0.524934 | 0.489510 | 26.888218 | 11.588261 | 51.143720 | 69.486405 | 60.595598 | 76.391886 | 93.029780 | 77.427708 | 82.088908 | 70.716444 | 89.512300 | 82.239965 | 81.635736 | 83.894646 | 85.304273 | 76.931377 | 76.413466 | 48.640483 | 78.161416 | 50.107898 | 45.986189 | 76.413466 | 76.132931 |
| 2 | Curtis Blaydes | Orthodox | No | No | No | No | 2.0 | 62.0 | 0.0 | 0.0 | 80.250000 | 395.0 | 69.0 | 110.0 | 260.0 | 73.0 | 241.0 | 574.0 | 1144.0 | 1016.0 | 1681.0 | 116.0 | 941.0 | 80.0 | 123.0 | 754.0 | 91.0 | 299.0 | 4.0 | 13.0 | 2.0 | 0.0 | 7.650000 | 224.0 | 47.0 | 27.0 | 242.0 | 41.0 | 15.0 | 298.0 | 739.0 | 530.0 | 985.0 | 19.0 | 640.0 | 67.0 | 32.0 | 668.0 | 52.0 | 19.0 | 6.0 | 416.0 | 20.0 | 5.0 | 426.0 | 11.0 | 4.0 | 441.0 | 455.0 | 0.534483 | 0.419766 | 0.862500 | 0.894309 | 0.344828 | 0.802198 | 0.806020 | 0.501748 | 0.604402 | 0.315789 | 0.650000 | 0.298507 | 0.156250 | 0.637725 | 0.211538 | 0.210526 | 0.596752 | 0.461929 | 76.542943 | 99.525248 | 21.255934 | 29.477773 | 98.575744 | 90.504963 | 77.924040 | 91.735002 | 79.909366 | 87.915408 | 99.050496 | 89.490721 | 92.706085 | 74.265976 | 50.841606 | 79.844627 | 64.911524 | 53.582218 | 80.146741 | 59.710833 | 33.448425 | 78.700906 | 78.485110 |
| 3 | Jailton Almeida | Orthodox | No | No | No | No | 0.0 | 25.0 | 8.0 | 1.0 | 47.950000 | 143.0 | 8.0 | 2.0 | 8.0 | 3.0 | 142.0 | 153.0 | 232.0 | 328.0 | 442.0 | 42.0 | 216.0 | 10.0 | 6.0 | 22.0 | 8.0 | 202.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.100000 | 34.0 | 3.0 | 1.0 | 4.0 | 1.0 | 33.0 | 38.0 | 68.0 | 74.0 | 115.0 | 2.0 | 61.0 | 6.0 | 1.0 | 19.0 | 1.0 | 48.0 | 2.0 | 27.0 | 3.0 | 0.0 | 15.0 | 0.0 | 15.0 | 30.0 | 41.0 | 0.595238 | 0.662037 | 0.800000 | 0.333333 | 0.363636 | 0.375000 | 0.702970 | 0.659483 | 0.742081 | 1.000000 | 0.442623 | 0.500000 | 0.000000 | 0.789474 | 0.000000 | 0.312500 | 0.441176 | 0.356522 | 26.888218 | 94.238239 | 91.562365 | 69.486405 | 94.216659 | 65.947346 | 24.622356 | 14.436772 | 11.631420 | 18.644799 | 96.266724 | 55.761761 | 64.954683 | 95.293610 | 28.226155 | 13.508848 | 23.327579 | 9.387139 | 11.242987 | 5.955978 | 68.558481 | 13.487268 | 15.537333 |
| 4 | Benoit Saint Denis | Southpaw | No | No | No | No | 4.0 | 16.0 | 5.0 | 1.0 | 24.416667 | 159.0 | 85.0 | 43.0 | 166.0 | 50.0 | 71.0 | 287.0 | 525.0 | 439.0 | 700.0 | 43.0 | 372.0 | 108.0 | 45.0 | 335.0 | 64.0 | 126.0 | 1.0 | 4.0 | 6.0 | 0.0 | 3.666667 | 159.0 | 67.0 | 25.0 | 193.0 | 50.0 | 8.0 | 251.0 | 439.0 | 299.0 | 497.0 | 13.0 | 336.0 | 76.0 | 27.0 | 360.0 | 71.0 | 8.0 | 9.0 | 177.0 | 9.0 | 2.0 | 167.0 | 21.0 | 0.0 | 188.0 | 198.0 | 0.372093 | 0.427419 | 0.787037 | 0.955556 | 0.495522 | 0.781250 | 0.563492 | 0.546667 | 0.627143 | 0.692308 | 0.526786 | 0.118421 | 0.074074 | 0.463889 | 0.295775 | 0.000000 | 0.428246 | 0.398390 | 89.404402 | 88.044886 | 83.707380 | 69.486405 | 81.614156 | 69.249029 | 82.585240 | 72.766508 | 67.695296 | 79.499353 | 86.944325 | 72.701770 | 73.176521 | 80.440415 | 61.609840 | 54.963315 | 44.993526 | 35.930082 | 54.294346 | 77.470868 | 6.689685 | 53.970652 | 53.172205 |
| 5 | Dustin Poirier | Southpaw | No | Yes | Yes | Yes | 14.0 | 28.0 | 24.0 | 4.0 | 69.400000 | 1343.0 | 158.0 | 177.0 | 1262.0 | 254.0 | 162.0 | 1678.0 | 3284.0 | 2010.0 | 3654.0 | 77.0 | 2850.0 | 219.0 | 215.0 | 2644.0 | 387.0 | 253.0 | 3.0 | 29.0 | 28.0 | 3.0 | 58.800000 | 864.0 | 293.0 | 184.0 | 1032.0 | 186.0 | 123.0 | 1341.0 | 2881.0 | 1780.0 | 3372.0 | 76.0 | 2257.0 | 404.0 | 220.0 | 2464.0 | 262.0 | 155.0 | 47.0 | 1393.0 | 111.0 | 36.0 | 1432.0 | 76.0 | 32.0 | 1540.0 | 1592.0 | 0.363636 | 0.471228 | 0.721461 | 0.823256 | 0.477307 | 0.656331 | 0.640316 | 0.510962 | 0.550082 | 0.618421 | 0.617191 | 0.274752 | 0.163636 | 0.581169 | 0.290076 | 0.206452 | 0.534537 | 0.472123 | 99.719465 | 95.317221 | 99.568407 | 95.684074 | 97.583082 | 99.913681 | 93.828226 | 96.935693 | 99.525248 | 99.568407 | 97.496763 | 99.697885 | 99.482089 | 56.174439 | 96.568839 | 98.662063 | 98.036254 | 95.274061 | 98.662063 | 98.273630 | 87.591713 | 98.748382 | 98.748382 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2252 | Ian Freeman | Orthodox | No | No | No | No | 0.0 | 3.0 | 1.0 | 1.0 | 17.100000 | 86.0 | 9.0 | 4.0 | 11.0 | 28.0 | 60.0 | 99.0 | 196.0 | 214.0 | 317.0 | 6.0 | 180.0 | 12.0 | 4.0 | 52.0 | 42.0 | 102.0 | 1.0 | 4.0 | 3.0 | 2.0 | 16.000000 | 39.0 | 33.0 | 27.0 | 28.0 | 32.0 | 39.0 | 99.0 | 165.0 | 185.0 | 257.0 | 26.0 | 88.0 | 41.0 | 36.0 | 65.0 | 44.0 | 56.0 | 22.0 | 49.0 | 8.0 | 9.0 | 37.0 | 12.0 | 17.0 | 66.0 | 72.0 | 0.500000 | 0.477778 | 0.750000 | 1.000000 | 0.211538 | 0.666667 | 0.588235 | 0.505102 | 0.675079 | 0.846154 | 0.556818 | 0.195122 | 0.250000 | 0.569231 | 0.272727 | 0.303571 | 0.400000 | 0.280156 | 26.888218 | 49.352611 | 51.143720 | 69.486405 | 73.457057 | 52.546396 | 26.866638 | 21.514890 | 14.134657 | 64.889944 | 83.340527 | 44.195080 | 51.855848 | 88.385147 | 83.211049 | 22.140699 | 42.166595 | 67.522659 | 20.371170 | 62.214070 | 72.637031 | 25.528701 | 25.183427 |
| 2264 | Eugene Jackson | Orthodox | No | No | No | No | 0.0 | 2.0 | 3.0 | 0.0 | 0.700000 | 3.0 | 1.0 | 0.0 | 2.0 | 1.0 | 1.0 | 4.0 | 17.0 | 73.0 | 89.0 | 3.0 | 15.0 | 2.0 | 0.0 | 14.0 | 1.0 | 2.0 | 1.0 | 4.0 | 4.0 | 0.0 | 12.233333 | 8.0 | 5.0 | 3.0 | 7.0 | 0.0 | 9.0 | 16.0 | 26.0 | 64.0 | 77.0 | 5.0 | 15.0 | 5.0 | 6.0 | 11.0 | 0.0 | 15.0 | 1.0 | 7.0 | 0.0 | 3.0 | 4.0 | 0.0 | 6.0 | 10.0 | 13.0 | 0.666667 | 0.200000 | 0.500000 | NaN | 0.142857 | 1.000000 | 0.500000 | 0.235294 | 0.820225 | 0.200000 | 0.466667 | 0.000000 | 0.500000 | 0.363636 | NaN | 0.400000 | 0.384615 | 0.168831 | 26.888218 | 40.850237 | 74.126025 | 29.477773 | 14.156237 | 6.258092 | 7.250755 | 3.366422 | 4.984894 | 11.005611 | 19.917997 | 4.359085 | 24.428140 | 98.272884 | 19.615883 | 3.690117 | 4.661200 | 42.878722 | 4.380665 | 5.955978 | 41.929219 | 4.315926 | 5.157531 |
| 2278 | Pat Miletich | Orthodox | Yes | No | Yes | Yes | 1.0 | 6.0 | 3.0 | 0.0 | 14.166667 | 47.0 | 6.0 | 11.0 | 40.0 | 9.0 | 15.0 | 64.0 | 140.0 | 202.0 | 281.0 | 6.0 | 120.0 | 7.0 | 13.0 | 107.0 | 14.0 | 19.0 | 0.0 | 5.0 | 3.0 | 0.0 | 7.366667 | 27.0 | 14.0 | 11.0 | 21.0 | 10.0 | 21.0 | 52.0 | 143.0 | 145.0 | 245.0 | 11.0 | 114.0 | 16.0 | 13.0 | 86.0 | 13.0 | 44.0 | 6.0 | 87.0 | 2.0 | 2.0 | 65.0 | 3.0 | 23.0 | 91.0 | 100.0 | 1.000000 | 0.391667 | 0.857143 | 0.846154 | 0.373832 | 0.642857 | 0.789474 | 0.457143 | 0.718861 | 0.545455 | 0.763158 | 0.125000 | 0.153846 | 0.755814 | 0.230769 | 0.522727 | 0.636364 | 0.408163 | 62.580924 | 67.393181 | 74.126025 | 29.477773 | 68.580060 | 37.440656 | 19.896418 | 37.958567 | 32.455762 | 36.426413 | 52.244281 | 32.606819 | 50.107898 | 93.523316 | 50.841606 | 35.369012 | 18.277946 | 35.930082 | 30.513595 | 28.441951 | 80.427277 | 33.858438 | 33.470004 |
| 2282 | Kevin Randleman | Orthodox | Yes | No | Yes | Yes | 0.0 | 5.0 | 3.0 | 0.0 | 25.866667 | 43.0 | 6.0 | 1.0 | 14.0 | 8.0 | 28.0 | 50.0 | 110.0 | 153.0 | 222.0 | 9.0 | 103.0 | 6.0 | 1.0 | 46.0 | 10.0 | 54.0 | 1.0 | 1.0 | 6.0 | 0.0 | 2.983333 | 23.0 | 9.0 | 13.0 | 14.0 | 16.0 | 15.0 | 45.0 | 106.0 | 115.0 | 180.0 | 1.0 | 81.0 | 10.0 | 15.0 | 65.0 | 19.0 | 22.0 | 0.0 | 58.0 | 1.0 | 2.0 | 51.0 | 3.0 | 7.0 | 61.0 | 65.0 | 0.555556 | 0.417476 | 1.000000 | 1.000000 | 0.304348 | 0.800000 | 0.518519 | 0.454545 | 0.689189 | 0.000000 | 0.716049 | 0.100000 | 0.133333 | 0.784615 | 0.157895 | 0.318182 | 0.575472 | 0.361111 | 26.888218 | 62.127751 | 74.126025 | 29.477773 | 82.693138 | 35.800604 | 19.896418 | 9.451877 | 16.659473 | 33.987915 | 66.724212 | 27.384549 | 42.986621 | 90.198618 | 7.466552 | 25.334484 | 12.473025 | 35.930082 | 24.838153 | 28.441951 | 45.986189 | 24.126025 | 23.047044 |
| 2286 | Fabiano Iha | Orthodox | No | No | No | No | 0.0 | 5.0 | 6.0 | 0.0 | 8.616667 | 24.0 | 3.0 | 10.0 | 12.0 | 13.0 | 12.0 | 37.0 | 79.0 | 73.0 | 121.0 | 11.0 | 62.0 | 6.0 | 11.0 | 38.0 | 22.0 | 19.0 | 0.0 | 3.0 | 0.0 | 0.0 | 7.716667 | 49.0 | 2.0 | 2.0 | 15.0 | 25.0 | 13.0 | 53.0 | 93.0 | 120.0 | 164.0 | 6.0 | 88.0 | 2.0 | 3.0 | 36.0 | 37.0 | 20.0 | 3.0 | 39.0 | 0.0 | 1.0 | 21.0 | 12.0 | 7.0 | 40.0 | 44.0 | 0.454545 | 0.387097 | 0.500000 | 0.909091 | 0.315789 | 0.590909 | 0.631579 | 0.468354 | 0.603306 | 0.500000 | 0.443182 | 0.000000 | 0.333333 | 0.583333 | 0.324324 | 0.350000 | 0.430108 | 0.268293 | 26.888218 | 62.127751 | 86.922745 | 29.477773 | 55.006474 | 24.384981 | 13.465688 | 36.102719 | 14.954683 | 44.950367 | 47.949935 | 21.579629 | 24.428140 | 73.920553 | 35.261114 | 18.580060 | 4.661200 | 25.377644 | 13.854122 | 62.214070 | 45.986189 | 17.457920 | 16.422098 |
1321 rows × 100 columns
c.describe()
| KD | TD | SUB.ATT | REV | CTRL | HEAD | BODY | LEG | DISTANCE | CLINCH | GROUND | SIG | TOT.SIG | STR | TOTAL.STR | TOTAL.TD | TOTAL.HEAD | TOTAL.BODY | TOTAL.LEG | TOTAL.DISTANCE | TOTAL.CLINCH | TOTAL.GROUND | OPP_KD | OPP_TD | OPP_SUB.ATT | OPP_REV | OPP_CTRL | OPP_HEAD | OPP_BODY | OPP_LEG | OPP_DISTANCE | OPP_CLINCH | OPP_GROUND | OPP_SIG | OPP_TOT.SIG | OPP_STR | OPP_TOTAL.STR | OPP_TOTAL.TD | OPP_TOTAL.HEAD | OPP_TOTAL.BODY | OPP_TOTAL.LEG | OPP_TOTAL.DISTANCE | OPP_TOTAL.CLINCH | OPP_TOTAL.GROUND | TD_DEF | HEAD_DEF | BODY_DEF | LEG_DEF | DISTANCE_DEF | CLINCH_DEF | GROUND_DEF | SIG_DEF | STR_DEF | TD_% | HEAD_% | BODY_% | LEG_% | DISTANCE_% | CLINCH_% | GROUND_% | SIG_% | STR_% | TD_DEF_% | HEAD_DEF_% | BODY_DEF_% | LEG_DEF_% | DISTANCE_DEF_% | CLINCH_DEF_% | GROUND_DEF_% | SIG_DEF_% | STR_DEF_% | KD_PERCENTILE | TD_PERCENTILE | SUB.ATT_PERCENTILE | REV_PERCENTILE | CTRL_PERCENTILE | HEAD_PERCENTILE | BODY_PERCENTILE | LEG_PERCENTILE | DISTANCE_PERCENTILE | CLINCH_PERCENTILE | GROUND_PERCENTILE | SIG_PERCENTILE | STR_PERCENTILE | STR_%_PERCENTILE | TD_DEF_PERCENTILE | HEAD_DEF_PERCENTILE | BODY_DEF_PERCENTILE | LEG_DEF_PERCENTILE | DISTANCE_DEF_PERCENTILE | CLINCH_DEF_PERCENTILE | GROUND_DEF_PERCENTILE | SIG_DEF_PERCENTILE | STR_DEF_PERCENTILE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1289.000000 | 1321.000000 | 1321.000000 | 1314.000000 | 1321.000000 | 1318.000000 | 1308.000000 | 1321.000000 | 1321.000000 | 1319.000000 | 1321.000000 | 1320.000000 | 1321.000000 | 1321.000000 | 1315.000000 | 1308.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 |
| mean | 2.252839 | 10.596518 | 3.781983 | 1.286147 | 22.030066 | 232.949281 | 75.190008 | 59.572294 | 266.233157 | 50.746404 | 50.732021 | 367.711582 | 824.610901 | 543.055261 | 1024.389856 | 27.595004 | 642.404996 | 108.452687 | 73.753217 | 678.369417 | 72.629069 | 73.612415 | 1.996972 | 9.981075 | 3.450416 | 1.267979 | 20.563096 | 221.841787 | 73.132475 | 58.787282 | 260.554126 | 48.726722 | 44.480696 | 353.761544 | 807.454958 | 519.335352 | 995.927328 | 27.336109 | 627.856927 | 106.520061 | 73.077971 | 672.462528 | 70.308857 | 64.683573 | 17.355034 | 406.015140 | 33.387585 | 14.290689 | 411.908403 | 21.582135 | 20.202877 | 453.693414 | 476.591976 | 0.391199 | 0.365860 | 0.701506 | 0.810749 | 0.385467 | 0.688273 | 0.686477 | 0.449923 | 0.537240 | 0.601871 | 0.632236 | 0.304079 | 0.188745 | 0.611299 | 0.307903 | 0.294140 | 0.547024 | 0.460158 | 62.648293 | 64.742954 | 61.812797 | 58.556462 | 66.622653 | 69.235339 | 67.773887 | 66.463378 | 68.158220 | 67.023174 | 67.203865 | 69.318571 | 69.562579 | 51.257519 | 66.023242 | 68.582298 | 67.197282 | 65.397808 | 68.244441 | 65.752476 | 61.479660 | 68.636533 | 68.778982 |
| std | 2.797936 | 12.094638 | 4.775982 | 1.735074 | 21.518480 | 206.074111 | 73.364291 | 64.177366 | 256.704216 | 51.291325 | 55.189324 | 318.010726 | 706.163668 | 437.079073 | 824.978180 | 30.025952 | 556.757267 | 105.465905 | 80.207654 | 621.034104 | 70.811884 | 79.410315 | 2.045688 | 8.668933 | 3.875037 | 1.741024 | 16.528213 | 177.951083 | 59.925306 | 53.229429 | 231.984711 | 43.066319 | 39.862831 | 274.672077 | 652.592225 | 362.823442 | 740.163124 | 23.091038 | 516.434098 | 88.937737 | 66.251352 | 591.595722 | 61.603952 | 59.203397 | 16.540045 | 348.218196 | 31.811799 | 15.154567 | 367.629078 | 20.419601 | 21.753746 | 387.552154 | 396.360506 | 0.199546 | 0.080352 | 0.109492 | 0.115944 | 0.079219 | 0.128892 | 0.141052 | 0.078441 | 0.091430 | 0.204568 | 0.081236 | 0.105665 | 0.102224 | 0.070787 | 0.116369 | 0.131679 | 0.073071 | 0.089467 | 26.283458 | 25.091221 | 26.854650 | 26.956676 | 23.168898 | 19.982243 | 21.640616 | 23.313468 | 21.324377 | 22.463853 | 22.174434 | 19.852317 | 19.548855 | 25.002859 | 23.569217 | 20.741931 | 22.517239 | 23.963256 | 21.204668 | 23.639517 | 26.330683 | 20.713706 | 20.568315 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.016667 | 3.000000 | 0.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | 4.000000 | 17.000000 | 20.000000 | 55.000000 | 0.000000 | 15.000000 | 2.000000 | 0.000000 | 14.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.016667 | 8.000000 | 0.000000 | 1.000000 | 4.000000 | 0.000000 | 0.000000 | 16.000000 | 26.000000 | 48.000000 | 77.000000 | 0.000000 | 15.000000 | 0.000000 | 1.000000 | 11.000000 | 0.000000 | 0.000000 | 0.000000 | 7.000000 | 0.000000 | 0.000000 | 4.000000 | 0.000000 | 0.000000 | 10.000000 | 13.000000 | 0.000000 | 0.110497 | 0.000000 | 0.000000 | 0.076923 | 0.000000 | 0.000000 | 0.204167 | 0.263804 | 0.000000 | 0.283019 | 0.000000 | 0.000000 | 0.359375 | 0.000000 | 0.000000 | 0.269841 | 0.156823 | 26.888218 | 11.588261 | 21.255934 | 29.477773 | 5.308589 | 6.258092 | 2.740613 | 3.366422 | 4.984894 | 4.294346 | 8.610272 | 4.359085 | 8.416055 | 2.245250 | 7.466552 | 3.690117 | 4.661200 | 9.387139 | 4.380665 | 5.955978 | 6.689685 | 4.315926 | 5.157531 |
| 25% | 0.000000 | 3.000000 | 1.000000 | 0.000000 | 7.500000 | 94.000000 | 27.000000 | 19.000000 | 96.000000 | 17.000000 | 15.000000 | 151.000000 | 337.000000 | 242.000000 | 446.000000 | 8.000000 | 260.000000 | 38.000000 | 24.000000 | 261.000000 | 26.000000 | 22.000000 | 0.000000 | 4.000000 | 1.000000 | 0.000000 | 9.433333 | 101.000000 | 31.000000 | 23.000000 | 103.000000 | 19.000000 | 16.000000 | 161.000000 | 359.000000 | 266.000000 | 493.000000 | 11.000000 | 273.000000 | 44.000000 | 29.000000 | 275.000000 | 27.000000 | 23.000000 | 6.000000 | 170.000000 | 12.000000 | 4.000000 | 164.000000 | 8.000000 | 6.000000 | 192.000000 | 210.000000 | 0.268657 | 0.313343 | 0.638158 | 0.754324 | 0.336842 | 0.622685 | 0.611896 | 0.399329 | 0.475073 | 0.481917 | 0.582979 | 0.238054 | 0.119048 | 0.567010 | 0.234043 | 0.213993 | 0.503226 | 0.404405 | 26.888218 | 49.352611 | 51.143720 | 29.477773 | 50.798446 | 55.394907 | 52.934830 | 51.100561 | 53.409581 | 51.402676 | 52.244281 | 55.351748 | 55.999137 | 30.310881 | 50.841606 | 54.229607 | 52.157963 | 48.640483 | 53.798015 | 50.107898 | 41.929219 | 54.251187 | 54.380665 |
| 50% | 1.000000 | 7.000000 | 2.000000 | 1.000000 | 15.566667 | 172.000000 | 52.000000 | 39.000000 | 186.000000 | 35.000000 | 33.000000 | 272.000000 | 608.000000 | 416.000000 | 776.000000 | 18.000000 | 474.000000 | 77.000000 | 48.000000 | 484.000000 | 50.000000 | 48.000000 | 1.000000 | 8.000000 | 2.000000 | 1.000000 | 16.400000 | 166.000000 | 57.000000 | 43.000000 | 188.000000 | 36.000000 | 34.000000 | 268.000000 | 606.000000 | 411.000000 | 769.000000 | 21.000000 | 467.000000 | 83.000000 | 52.000000 | 488.000000 | 53.000000 | 48.000000 | 12.000000 | 301.000000 | 24.000000 | 10.000000 | 299.000000 | 15.000000 | 14.000000 | 334.000000 | 358.000000 | 0.380952 | 0.364055 | 0.702128 | 0.823529 | 0.385321 | 0.694680 | 0.693082 | 0.448454 | 0.536972 | 0.625000 | 0.637612 | 0.303436 | 0.180000 | 0.611111 | 0.304000 | 0.294118 | 0.552867 | 0.463816 | 62.580924 | 70.975399 | 65.105740 | 69.486405 | 70.608546 | 71.299094 | 70.608546 | 70.155373 | 70.975399 | 70.716444 | 70.414329 | 71.320673 | 71.342253 | 51.986183 | 69.227449 | 71.428571 | 69.917997 | 70.241692 | 71.126457 | 68.774277 | 66.055244 | 71.406992 | 71.471731 |
| 75% | 3.000000 | 14.000000 | 5.000000 | 2.000000 | 30.366667 | 299.000000 | 98.000000 | 76.000000 | 337.000000 | 65.000000 | 66.000000 | 467.000000 | 1052.000000 | 700.000000 | 1328.000000 | 36.000000 | 838.000000 | 143.000000 | 94.000000 | 871.000000 | 93.000000 | 98.000000 | 3.000000 | 14.000000 | 5.000000 | 2.000000 | 26.716667 | 290.000000 | 96.000000 | 77.000000 | 338.000000 | 66.000000 | 58.000000 | 454.000000 | 1040.000000 | 678.000000 | 1273.000000 | 37.000000 | 820.000000 | 139.000000 | 96.000000 | 885.000000 | 97.000000 | 87.000000 | 24.000000 | 538.000000 | 44.000000 | 20.000000 | 548.000000 | 29.000000 | 27.000000 | 601.000000 | 622.000000 | 0.500000 | 0.416667 | 0.767857 | 0.883877 | 0.436813 | 0.772190 | 0.768897 | 0.499382 | 0.596290 | 0.740741 | 0.684669 | 0.370370 | 0.250000 | 0.658960 | 0.375000 | 0.375000 | 0.595300 | 0.522124 | 84.570565 | 85.476910 | 83.707380 | 84.613725 | 85.757445 | 85.779025 | 85.714286 | 85.843763 | 85.757445 | 85.951662 | 85.606388 | 85.735865 | 85.757445 | 71.718480 | 85.304273 | 85.800604 | 85.779025 | 85.973241 | 85.779025 | 85.520069 | 84.354769 | 85.757445 | 85.757445 |
| max | 20.000000 | 90.000000 | 48.000000 | 13.000000 | 162.116667 | 2117.000000 | 723.000000 | 508.000000 | 2709.000000 | 453.000000 | 461.000000 | 3122.000000 | 6586.000000 | 3366.000000 | 6859.000000 | 274.000000 | 5254.000000 | 964.000000 | 630.000000 | 6034.000000 | 576.000000 | 656.000000 | 12.000000 | 74.000000 | 29.000000 | 12.000000 | 128.183333 | 1310.000000 | 454.000000 | 486.000000 | 1930.000000 | 293.000000 | 273.000000 | 2086.000000 | 5151.000000 | 2460.000000 | 5276.000000 | 165.000000 | 4005.000000 | 770.000000 | 611.000000 | 4894.000000 | 437.000000 | 420.000000 | 126.000000 | 2695.000000 | 316.000000 | 128.000000 | 2964.000000 | 162.000000 | 163.000000 | 3065.000000 | 3085.000000 | 1.000000 | 0.769231 | 1.000000 | 1.000000 | 0.680769 | 1.000000 | 1.000000 | 0.797101 | 0.824593 | 1.000000 | 0.902913 | 1.000000 | 0.600000 | 0.856707 | 1.000000 | 1.000000 | 0.813889 | 0.758883 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 98.402418 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 |
# Selecting general performance indicators based on available columns
metrics = [
"KD", "TD", "SUB.ATT", "REV", "CTRL", "HEAD", "BODY", "LEG", "DISTANCE",
"CLINCH", "GROUND", "SIG", "STR", "TD_DEF", "HEAD_DEF", "BODY_DEF", "LEG_DEF",
"DISTANCE_DEF", "CLINCH_DEF", "GROUND_DEF", "SIG_DEF", "STR_DEF", "TD_%",
"HEAD_%", "BODY_%", "LEG_%", "DISTANCE_%", "CLINCH_%", "GROUND_%", "SIG_%",
"STR_%", "TD_DEF_%", "HEAD_DEF_%", "BODY_DEF_%", "LEG_DEF_%", "DISTANCE_DEF_%",
"CLINCH_DEF_%", "GROUND_DEF_%", "SIG_DEF_%", "STR_DEF_%"
]
# Reinitializing the dictionary to hold the top ten fighters for the updated list of metrics
top_fighters_per_metric = {}
# Creating a dictionary to hold the top ten fighters for each metric
top_fighters_per_metric = {}
# Finding the top ten fighters for each metric
for metric in metrics:
# Some metrics may have missing values, so we sort only non-NA values
top_fighters_per_metric[metric] = c.nlargest(10, metric)[['FIGHTER', metric]]
# Printing the top ten fighters for each metric with a blank line in between
for metric, data in top_fighters_per_metric.items():
print(f"Top ten fighters for {metric}:")
if isinstance(data, pd.DataFrame):
print(data.to_string(index=False))
else:
print(data)
print("\n") # Blank line for separation
Top ten fighters for KD:
FIGHTER KD
Donald Cerrone 20.0
Jeremy Stephens 18.0
Anderson Silva 18.0
Edson Barboza 16.0
Dustin Poirier 14.0
Mauricio Rua 14.0
Thiago Santos 14.0
Junior Dos Santos 14.0
Lyoto Machida 14.0
Chuck Liddell 14.0
Top ten fighters for TD:
FIGHTER TD
Georges St-Pierre 90.0
Gleison Tibau 84.0
Merab Dvalishvili 79.0
Clay Guida 78.0
Demetrious Johnson 74.0
Frankie Edgar 73.0
Colby Covington 69.0
Nik Lentz 69.0
Rafael Dos Anjos 68.0
Demian Maia 68.0
Top ten fighters for SUB.ATT:
FIGHTER SUB.ATT
Jim Miller 48.0
Charles Oliveira 40.0
Chris Lytle 31.0
Joe Lauzon 29.0
Demian Maia 27.0
Nate Diaz 26.0
Nik Lentz 26.0
Darren Elkins 25.0
Dustin Poirier 24.0
Matt Brown 24.0
Top ten fighters for REV:
FIGHTER REV
Louis Smolka 13.0
Alex Caceres 12.0
Anthony Pettis 11.0
Dustin Ortiz 11.0
Brendan Allen 9.0
Carlos Condit 9.0
Cole Miller 9.0
Chris Lytle 9.0
Paul Kelly 9.0
Kelvin Gastelum 8.0
Top ten fighters for CTRL:
FIGHTER CTRL
Georges St-Pierre 162.116667
Clay Guida 156.500000
Demian Maia 155.266667
Rafael Dos Anjos 136.750000
Kamaru Usman 133.983333
Jon Fitch 128.383333
Randy Couture 126.766667
Darren Elkins 121.766667
Colby Covington 121.550000
Matt Hughes 117.800000
Top ten fighters for HEAD:
FIGHTER HEAD
Max Holloway 2117.0
Sean Strickland 1558.0
Dustin Poirier 1343.0
Frankie Edgar 1195.0
Nate Diaz 1146.0
Angela Hill 1136.0
Michael Bisping 1132.0
Rafael Dos Anjos 1061.0
Evan Dunham 1039.0
Jessica Andrade 1021.0
Top ten fighters for BODY:
FIGHTER BODY
Max Holloway 723.0
Angela Hill 594.0
Michael Johnson 451.0
Edson Barboza 449.0
Donald Cerrone 442.0
Joanna Jedrzejczyk 435.0
Rafael Dos Anjos 433.0
Bobby Green 412.0
Tim Means 402.0
Aljamain Sterling 390.0
Top ten fighters for LEG:
FIGHTER LEG
Joanna Jedrzejczyk 508.0
Joanne Wood 425.0
Jon Jones 420.0
Donald Cerrone 413.0
Alexander Volkanovski 407.0
Pedro Munhoz 405.0
Chris Gutierrez 389.0
Thiago Alves 387.0
Israel Adesanya 363.0
Andrei Arlovski 357.0
Top ten fighters for DISTANCE:
FIGHTER DISTANCE
Max Holloway 2709.0
Sean Strickland 1712.0
Donald Cerrone 1478.0
Joanna Jedrzejczyk 1431.0
Angela Hill 1380.0
Bobby Green 1353.0
Edson Barboza 1303.0
Andrei Arlovski 1298.0
Michael Johnson 1285.0
Michael Bisping 1271.0
Top ten fighters for CLINCH:
FIGHTER CLINCH
Angela Hill 453.0
Demetrious Johnson 359.0
Raquel Pennington 354.0
Nate Diaz 342.0
Rafael Dos Anjos 321.0
Joanna Jedrzejczyk 307.0
Jessica Andrade 296.0
Robbie Lawler 288.0
Matt Brown 276.0
Neil Magny 263.0
Top ten fighters for GROUND:
FIGHTER GROUND
Georges St-Pierre 461.0
Frankie Edgar 409.0
Khabib Nurmagomedov 380.0
Glover Teixeira 373.0
Kamaru Usman 345.0
Diego Sanchez 334.0
Neil Magny 332.0
Jon Fitch 308.0
Randy Couture 289.0
Demian Maia 279.0
Top ten fighters for SIG:
FIGHTER SIG
Max Holloway 3122.0
Angela Hill 1896.0
Sean Strickland 1887.0
Rafael Dos Anjos 1818.0
Frankie Edgar 1801.0
Joanna Jedrzejczyk 1754.0
Donald Cerrone 1748.0
Dustin Poirier 1678.0
Bobby Green 1668.0
Jessica Andrade 1591.0
Top ten fighters for STR:
FIGHTER STR
Max Holloway 3366.0
Georges St-Pierre 2591.0
Rafael Dos Anjos 2587.0
Neil Magny 2525.0
Darren Elkins 2501.0
Nate Diaz 2487.0
Frankie Edgar 2481.0
Kamaru Usman 2369.0
Angela Hill 2200.0
Jon Fitch 2185.0
Top ten fighters for TD_DEF:
FIGHTER TD_DEF
Max Holloway 126.0
Petr Yan 109.0
Joanna Jedrzejczyk 109.0
BJ Penn 109.0
Sam Stout 102.0
Brad Tavares 96.0
Rafael Dos Anjos 91.0
Michael Johnson 86.0
Jose Aldo 86.0
Angela Hill 85.0
Top ten fighters for HEAD_DEF:
FIGHTER HEAD_DEF
Max Holloway 2695.0
Frankie Edgar 2452.0
Angela Hill 2426.0
Rafael Dos Anjos 2348.0
Robbie Lawler 2106.0
Clay Guida 1967.0
Sean Strickland 1937.0
John Makdessi 1925.0
Ross Pearson 1853.0
Edson Barboza 1852.0
Top ten fighters for BODY_DEF:
FIGHTER BODY_DEF
Sean Strickland 316.0
Angela Hill 258.0
Max Holloway 245.0
Donald Cerrone 224.0
Pedro Munhoz 181.0
Rafael Dos Anjos 178.0
Bobby Green 167.0
John Dodson 164.0
Karolina Kowalkiewicz 161.0
Raquel Pennington 160.0
Top ten fighters for LEG_DEF:
FIGHTER LEG_DEF
Valentina Shevchenko 128.0
Max Holloway 125.0
Sean Strickland 102.0
Jose Aldo 91.0
Lyoto Machida 89.0
Joanna Jedrzejczyk 88.0
Jon Jones 86.0
Bobby Green 82.0
Dennis Siver 79.0
Katlyn Chookagian 76.0
Top ten fighters for DISTANCE_DEF:
FIGHTER DISTANCE_DEF
Max Holloway 2964.0
Angela Hill 2557.0
Frankie Edgar 2501.0
Rafael Dos Anjos 2405.0
Sean Strickland 2298.0
Robbie Lawler 2148.0
John Makdessi 2108.0
Clay Guida 1978.0
Ross Pearson 1907.0
Edson Barboza 1905.0
Top ten fighters for CLINCH_DEF:
FIGHTER CLINCH_DEF
Angela Hill 162.0
Robbie Lawler 144.0
Jon Jones 134.0
Rashad Evans 121.0
Nick Diaz 113.0
BJ Penn 110.0
Raquel Pennington 108.0
Jessica Andrade 108.0
Joe Lauzon 108.0
Nate Diaz 107.0
Top ten fighters for GROUND_DEF:
FIGHTER GROUND_DEF
Nate Diaz 163.0
Elvis Sinosic 144.0
Hermes Franca 142.0
Tito Ortiz 139.0
Diego Sanchez 131.0
Iuri Alcantara 127.0
Spencer Fisher 125.0
Germaine de Randamie 118.0
Anderson Silva 117.0
BJ Penn 114.0
Top ten fighters for SIG_DEF:
FIGHTER SIG_DEF
Max Holloway 3065.0
Angela Hill 2743.0
Frankie Edgar 2605.0
Rafael Dos Anjos 2551.0
Sean Strickland 2355.0
Robbie Lawler 2307.0
John Makdessi 2146.0
Clay Guida 2085.0
Ross Pearson 2037.0
Edson Barboza 2032.0
Top ten fighters for STR_DEF:
FIGHTER STR_DEF
Max Holloway 3085.0
Angela Hill 2845.0
Rafael Dos Anjos 2650.0
Frankie Edgar 2634.0
Sean Strickland 2377.0
Robbie Lawler 2331.0
Clay Guida 2157.0
John Makdessi 2151.0
Edson Barboza 2113.0
Ross Pearson 2067.0
Top ten fighters for TD_%:
FIGHTER TD_%
Johnny Walker 1.0
Sumudaerji 1.0
Jared Gooden 1.0
Uros Medic 1.0
Alex Pereira 1.0
Tom Aspinall 1.0
Andre Fialho 1.0
Omar Morales 1.0
Cheyanne Vlismas 1.0
Mariya Agapova 1.0
Top ten fighters for HEAD_%:
FIGHTER HEAD_%
Danilo Marques 0.769231
Umar Nurmagomedov 0.681633
Jailton Almeida 0.662037
Tatsuro Taira 0.659898
Cyrille Diabate 0.658228
Brock Lesnar 0.652361
Alistair Overeem 0.650927
Manny Bermudez 0.590164
Jerrod Sanders 0.586207
Tom Aspinall 0.583333
Top ten fighters for BODY_%:
FIGHTER BODY_%
Jeremy Kimball 1.0
Jerrod Sanders 1.0
Paul Sass 1.0
Ken Stone 1.0
Shane Carwin 1.0
Chris Tuchscherer 1.0
Jake O'Brien 1.0
Alvin Robinson 1.0
Rory Singer 1.0
Marcio Cruz 1.0
Top ten fighters for LEG_%:
FIGHTER LEG_%
Nikolas Motta 1.0
Martin Buday 1.0
Steve Garcia 1.0
Joe Solecki 1.0
Uros Medic 1.0
Sergei Pavlovich 1.0
Tom Aspinall 1.0
Nate Maness 1.0
Jacob Malkoun 1.0
Zarah Fairn 1.0
Top ten fighters for DISTANCE_%:
FIGHTER DISTANCE_%
Cyrille Diabate 0.680769
Danilo Marques 0.675000
Alistair Overeem 0.651452
Tatsuro Taira 0.630000
Tom Aspinall 0.620968
Jordan Leavitt 0.610811
Sean O'Malley 0.610329
Umar Nurmagomedov 0.596026
Sean Soriano 0.595349
Ciryl Gane 0.591365
Top ten fighters for CLINCH_%:
FIGHTER CLINCH_%
Umar Nurmagomedov 1.0
Joe Pyfer 1.0
Mike Malott 1.0
Yohan Lainesse 1.0
Rafael Alves 1.0
Michael Chandler 1.0
Vanessa Melo 1.0
Chance Rencountre 1.0
Alvaro Herrera Mendoza 1.0
Dongi Yang 1.0
Top ten fighters for GROUND_%:
FIGHTER GROUND_%
Marcin Prachnio 1.0
Zac Pauga 1.0
Yohan Lainesse 1.0
Carlos Hernandez 1.0
Trey Ogden 1.0
Modestas Bukauskas 1.0
Hakeem Dawodu 1.0
JP Buys 1.0
Aleksa Camur 1.0
Journey Newson 1.0
Top ten fighters for SIG_%:
FIGHTER SIG_%
Danilo Marques 0.797101
Alistair Overeem 0.743243
Cyrille Diabate 0.727794
Brock Lesnar 0.719595
Tatsuro Taira 0.710204
Umar Nurmagomedov 0.699367
Bartosz Fabinski 0.680723
Darrick Minner 0.674556
Tom Aspinall 0.666667
Ryan Jimmo 0.661342
Top ten fighters for STR_%:
FIGHTER STR_%
Brock Lesnar 0.824593
Eugene Jackson 0.820225
Mike Guymon 0.813830
Aaron Phillips 0.792910
Bartosz Fabinski 0.792208
Pat Sabatini 0.789168
Darrick Minner 0.779605
Tony DeSouza 0.778947
Alistair Overeem 0.778512
Charlie Brenneman 0.773613
Top ten fighters for TD_DEF_%:
FIGHTER TD_DEF_%
Jailton Almeida 1.0
Philipe Lins 1.0
Umar Nurmagomedov 1.0
Yazmin Jauregui 1.0
Gregory Rodrigues 1.0
Rodolfo Vieira 1.0
Zac Pauga 1.0
Martin Buday 1.0
Shavkat Rakhmonov 1.0
Trey Ogden 1.0
Top ten fighters for HEAD_DEF_%:
FIGHTER HEAD_DEF_%
Jon Madsen 0.902913
Umar Nurmagomedov 0.878049
Miguel Torres 0.861607
Adriano Martins 0.836449
Claude Patrick 0.825243
Nate Mohr 0.816901
Montel Jackson 0.814978
Desmond Green 0.813780
Fares Ziam 0.812500
Tom Aspinall 0.812500
Top ten fighters for BODY_DEF_%:
FIGHTER BODY_DEF_%
Jason Witt 1.000000
Ivan Salaverry 0.714286
Hunter Azure 0.641509
Frank Trigg 0.625000
Viscardi Andrade 0.611111
Luana Pinheiro 0.608696
Kajan Johnson 0.607143
Mike Guymon 0.600000
Phil Harris 0.589744
Nate Mohr 0.578947
Top ten fighters for LEG_DEF_%:
FIGHTER LEG_DEF_%
Jason Witt 0.600000
Vaughan Lee 0.600000
Mike Malott 0.571429
Kyle Bradley 0.571429
Muhammad Mokaev 0.562500
Anthony Birchak 0.545455
Jordan Leavitt 0.540541
Tatiana Suarez 0.533333
Naoyuki Kotani 0.529412
Darrick Minner 0.500000
Top ten fighters for DISTANCE_DEF_%:
FIGHTER DISTANCE_DEF_%
Jon Madsen 0.856707
Marcio Cruz 0.830189
Claude Patrick 0.827273
Ivan Salaverry 0.824675
Rory Singer 0.812081
Miguel Torres 0.809524
Jailton Almeida 0.789474
Luigi Fioravanti 0.788955
Luke Cummo 0.788732
Kevin Randleman 0.784615
Top ten fighters for CLINCH_DEF_%:
FIGHTER CLINCH_DEF_%
Trey Ogden 1.000000
Adrian Yanez 0.800000
Ryan Jensen 0.750000
Tony Ferguson 0.687500
Jason Gonzalez 0.687500
Ben Nguyen 0.684211
Mike Malott 0.666667
Kazula Vargas 0.666667
Greg Hardy 0.666667
Curtis Millender 0.666667
Top ten fighters for GROUND_DEF_%:
FIGHTER GROUND_DEF_%
Daniel Zellhuber 1.000000
Michael Morales 1.000000
Trey Ogden 1.000000
Tom Aspinall 1.000000
Rashid Magomedov 1.000000
Sergey Morozov 0.750000
Fredy Serrano 0.714286
Darren Uyenoyama 0.714286
Kennedy Nzechukwu 0.666667
Victoria Leonardo 0.666667
Top ten fighters for SIG_DEF_%:
FIGHTER SIG_DEF_%
Jon Madsen 0.813889
Umar Nurmagomedov 0.761468
Miguel Torres 0.755245
Nate Mohr 0.749245
Georges St-Pierre 0.730727
Phil Davis 0.723457
Ivan Salaverry 0.717703
Ryan Bader 0.717542
Danielle Taylor 0.716895
Miles Johns 0.715736
Top ten fighters for STR_DEF_%:
FIGHTER STR_DEF_%
Jon Madsen 0.758883
Nate Mohr 0.710227
Aiemann Zahabi 0.702062
Danielle Taylor 0.700000
Miles Johns 0.681710
Adriano Martins 0.680233
John Makdessi 0.671558
Phil Davis 0.669291
Vinny Magalhaes 0.661448
Joe Soto 0.657095
# Champions
c_champions = career[career['EITHER_CHAMP'] == 'Yes']
c_champions
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | KD | TD | SUB.ATT | REV | CTRL | HEAD | BODY | LEG | DISTANCE | CLINCH | GROUND | SIG | TOT.SIG | STR | TOTAL.STR | TOTAL.TD | TOTAL.HEAD | TOTAL.BODY | TOTAL.LEG | TOTAL.DISTANCE | TOTAL.CLINCH | TOTAL.GROUND | OPP_KD | OPP_TD | OPP_SUB.ATT | OPP_REV | OPP_CTRL | OPP_HEAD | OPP_BODY | OPP_LEG | OPP_DISTANCE | OPP_CLINCH | OPP_GROUND | OPP_SIG | OPP_TOT.SIG | OPP_STR | OPP_TOTAL.STR | OPP_TOTAL.TD | OPP_TOTAL.HEAD | OPP_TOTAL.BODY | OPP_TOTAL.LEG | OPP_TOTAL.DISTANCE | OPP_TOTAL.CLINCH | OPP_TOTAL.GROUND | TD_DEF | HEAD_DEF | BODY_DEF | LEG_DEF | DISTANCE_DEF | CLINCH_DEF | GROUND_DEF | SIG_DEF | STR_DEF | TD_% | HEAD_% | BODY_% | LEG_% | DISTANCE_% | CLINCH_% | GROUND_% | SIG_% | STR_% | TD_DEF_% | HEAD_DEF_% | BODY_DEF_% | LEG_DEF_% | DISTANCE_DEF_% | CLINCH_DEF_% | GROUND_DEF_% | SIG_DEF_% | STR_DEF_% | KD_PERCENTILE | TD_PERCENTILE | SUB.ATT_PERCENTILE | REV_PERCENTILE | CTRL_PERCENTILE | HEAD_PERCENTILE | BODY_PERCENTILE | LEG_PERCENTILE | DISTANCE_PERCENTILE | CLINCH_PERCENTILE | GROUND_PERCENTILE | SIG_PERCENTILE | STR_PERCENTILE | STR_%_PERCENTILE | TD_DEF_PERCENTILE | HEAD_DEF_PERCENTILE | BODY_DEF_PERCENTILE | LEG_DEF_PERCENTILE | DISTANCE_DEF_PERCENTILE | CLINCH_DEF_PERCENTILE | GROUND_DEF_PERCENTILE | SIG_DEF_PERCENTILE | STR_DEF_PERCENTILE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5 | Dustin Poirier | Southpaw | No | Yes | Yes | Yes | 14.0 | 28.0 | 24.0 | 4.0 | 69.400000 | 1343.0 | 158.0 | 177.0 | 1262.0 | 254.0 | 162.0 | 1678.0 | 3284.0 | 2010.0 | 3654.0 | 77.0 | 2850.0 | 219.0 | 215.0 | 2644.0 | 387.0 | 253.0 | 3.0 | 29.0 | 28.0 | 3.0 | 58.800000 | 864.0 | 293.0 | 184.0 | 1032.0 | 186.0 | 123.0 | 1341.0 | 2881.0 | 1780.0 | 3372.0 | 76.0 | 2257.0 | 404.0 | 220.0 | 2464.0 | 262.0 | 155.0 | 47.0 | 1393.0 | 111.0 | 36.0 | 1432.0 | 76.0 | 32.0 | 1540.0 | 1592.0 | 0.363636 | 0.471228 | 0.721461 | 0.823256 | 0.477307 | 0.656331 | 0.640316 | 0.510962 | 0.550082 | 0.618421 | 0.617191 | 0.274752 | 0.163636 | 0.581169 | 0.290076 | 0.206452 | 0.534537 | 0.472123 | 99.719465 | 95.317221 | 99.568407 | 95.684074 | 97.583082 | 99.913681 | 93.828226 | 96.935693 | 99.525248 | 99.568407 | 97.496763 | 99.697885 | 99.482089 | 56.174439 | 96.568839 | 98.662063 | 98.036254 | 95.274061 | 98.662063 | 98.273630 | 87.591713 | 98.748382 | 98.748382 |
| 17 | Rafael Dos Anjos | Southpaw | Yes | No | Yes | Yes | 7.0 | 68.0 | 20.0 | 3.0 | 136.750000 | 1061.0 | 433.0 | 324.0 | 1220.0 | 321.0 | 277.0 | 1818.0 | 3858.0 | 2587.0 | 4723.0 | 191.0 | 2880.0 | 588.0 | 390.0 | 3046.0 | 407.0 | 405.0 | 2.0 | 74.0 | 2.0 | 2.0 | 125.066667 | 1090.0 | 349.0 | 235.0 | 1321.0 | 235.0 | 118.0 | 1674.0 | 4225.0 | 2460.0 | 5110.0 | 165.0 | 3438.0 | 527.0 | 260.0 | 3726.0 | 324.0 | 175.0 | 91.0 | 2348.0 | 178.0 | 25.0 | 2405.0 | 89.0 | 57.0 | 2551.0 | 2650.0 | 0.356021 | 0.368403 | 0.736395 | 0.830769 | 0.400525 | 0.788698 | 0.683951 | 0.471229 | 0.547745 | 0.551515 | 0.682955 | 0.337761 | 0.096154 | 0.645464 | 0.274691 | 0.325714 | 0.603787 | 0.518591 | 95.899871 | 99.633146 | 99.179974 | 91.842900 | 99.870522 | 99.697885 | 99.741044 | 99.482089 | 99.438930 | 99.827363 | 99.568407 | 99.870522 | 99.913681 | 55.181347 | 99.741044 | 99.870522 | 99.784204 | 90.526543 | 99.870522 | 99.093656 | 96.331463 | 99.870522 | 99.913681 |
| 22 | Petr Yan | Switch | Yes | Yes | Yes | Yes | 10.0 | 25.0 | 2.0 | 2.0 | 32.866667 | 758.0 | 231.0 | 126.0 | 806.0 | 122.0 | 187.0 | 1115.0 | 2083.0 | 1438.0 | 2437.0 | 49.0 | 1665.0 | 284.0 | 134.0 | 1700.0 | 154.0 | 229.0 | 1.0 | 19.0 | 0.0 | 2.0 | 25.266667 | 592.0 | 216.0 | 138.0 | 840.0 | 75.0 | 31.0 | 946.0 | 2291.0 | 1103.0 | 2473.0 | 128.0 | 1784.0 | 329.0 | 178.0 | 2143.0 | 106.0 | 42.0 | 109.0 | 1192.0 | 113.0 | 40.0 | 1303.0 | 31.0 | 11.0 | 1345.0 | 1370.0 | 0.510204 | 0.455255 | 0.813380 | 0.940299 | 0.474118 | 0.792208 | 0.816594 | 0.535286 | 0.590070 | 0.851562 | 0.668161 | 0.343465 | 0.224719 | 0.608026 | 0.292453 | 0.261905 | 0.587080 | 0.553983 | 98.511006 | 94.238239 | 65.105740 | 84.613725 | 87.915408 | 98.446267 | 97.561502 | 93.396634 | 97.237808 | 94.885628 | 98.359948 | 97.928356 | 97.000432 | 69.689119 | 99.913681 | 97.755719 | 98.165732 | 96.288304 | 98.144152 | 87.030643 | 58.761329 | 97.798878 | 97.755719 |
| 27 | Sean O'Malley | Switch | Yes | No | Yes | Yes | 6.0 | 3.0 | 3.0 | 1.0 | 1.966667 | 668.0 | 208.0 | 88.0 | 910.0 | 19.0 | 35.0 | 964.0 | 1556.0 | 991.0 | 1589.0 | 7.0 | 1199.0 | 265.0 | 92.0 | 1491.0 | 23.0 | 42.0 | 0.0 | 11.0 | 0.0 | 0.0 | 14.166667 | 192.0 | 74.0 | 165.0 | 391.0 | 17.0 | 23.0 | 431.0 | 1145.0 | 502.0 | 1226.0 | 28.0 | 786.0 | 154.0 | 205.0 | 1081.0 | 33.0 | 31.0 | 17.0 | 594.0 | 80.0 | 40.0 | 690.0 | 16.0 | 8.0 | 714.0 | 724.0 | 0.428571 | 0.557131 | 0.784906 | 0.956522 | 0.610329 | 0.826087 | 0.833333 | 0.619537 | 0.623663 | 0.607143 | 0.755725 | 0.519481 | 0.195122 | 0.638298 | 0.484848 | 0.258065 | 0.623581 | 0.590538 | 94.475615 | 49.352611 | 74.126025 | 69.486405 | 24.298662 | 97.626241 | 96.849374 | 88.066465 | 98.230470 | 54.251187 | 71.968062 | 96.633578 | 92.166595 | 79.620035 | 77.643505 | 88.239102 | 95.640915 | 96.288304 | 91.044454 | 70.543807 | 49.827363 | 89.922313 | 89.339663 |
| 50 | Brandon Moreno | Orthodox | Yes | Yes | Yes | Yes | 4.0 | 30.0 | 8.0 | 6.0 | 52.816667 | 738.0 | 162.0 | 99.0 | 856.0 | 56.0 | 87.0 | 999.0 | 2268.0 | 1307.0 | 2599.0 | 64.0 | 1925.0 | 223.0 | 120.0 | 2032.0 | 102.0 | 134.0 | 5.0 | 24.0 | 3.0 | 3.0 | 29.666667 | 617.0 | 179.0 | 142.0 | 820.0 | 40.0 | 78.0 | 938.0 | 2327.0 | 1176.0 | 2610.0 | 66.0 | 1873.0 | 257.0 | 197.0 | 2162.0 | 69.0 | 96.0 | 42.0 | 1256.0 | 78.0 | 55.0 | 1342.0 | 29.0 | 18.0 | 1389.0 | 1434.0 | 0.468750 | 0.383377 | 0.726457 | 0.825000 | 0.421260 | 0.549020 | 0.649254 | 0.440476 | 0.502886 | 0.636364 | 0.670582 | 0.303502 | 0.279188 | 0.620722 | 0.420290 | 0.187500 | 0.596906 | 0.549425 | 89.404402 | 95.813552 | 91.562365 | 98.532585 | 95.166163 | 98.359948 | 94.195080 | 90.030211 | 97.734139 | 82.261545 | 90.051791 | 97.022011 | 95.964609 | 40.241796 | 95.533017 | 98.100993 | 95.295641 | 98.575744 | 98.230470 | 85.520069 | 74.492879 | 98.230470 | 98.252050 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2241 | Carlos Newton | Orthodox | Yes | No | Yes | Yes | 0.0 | 7.0 | 4.0 | 3.0 | 11.750000 | 11.0 | 9.0 | 3.0 | 11.0 | 9.0 | 3.0 | 23.0 | 77.0 | 98.0 | 158.0 | 13.0 | 64.0 | 10.0 | 3.0 | 55.0 | 14.0 | 8.0 | 0.0 | 11.0 | 3.0 | 1.0 | 30.566667 | 90.0 | 18.0 | 9.0 | 31.0 | 8.0 | 78.0 | 117.0 | 205.0 | 295.0 | 398.0 | 14.0 | 177.0 | 19.0 | 9.0 | 88.0 | 11.0 | 106.0 | 3.0 | 87.0 | 1.0 | 0.0 | 57.0 | 3.0 | 28.0 | 88.0 | 103.0 | 0.538462 | 0.171875 | 0.900000 | 1.000000 | 0.200000 | 0.642857 | 0.375000 | 0.298701 | 0.620253 | 0.214286 | 0.491525 | 0.052632 | 0.000000 | 0.647727 | 0.272727 | 0.264151 | 0.429268 | 0.258794 | 26.888218 | 70.975399 | 79.974104 | 91.842900 | 63.379370 | 13.875701 | 26.866638 | 18.191627 | 14.134657 | 36.426413 | 29.261977 | 15.062581 | 31.053086 | 78.972366 | 35.261114 | 35.369012 | 12.473025 | 9.387139 | 27.406129 | 28.441951 | 85.282693 | 33.146310 | 34.225291 |
| 2246 | Ricco Rodriguez | Orthodox | Yes | No | Yes | Yes | 0.0 | 17.0 | 6.0 | 2.0 | 39.600000 | 142.0 | 25.0 | 11.0 | 43.0 | 26.0 | 109.0 | 178.0 | 314.0 | 473.0 | 626.0 | 42.0 | 268.0 | 34.0 | 12.0 | 113.0 | 34.0 | 167.0 | 1.0 | 8.0 | 1.0 | 1.0 | 21.550000 | 67.0 | 13.0 | 29.0 | 38.0 | 17.0 | 54.0 | 109.0 | 259.0 | 235.0 | 391.0 | 13.0 | 209.0 | 15.0 | 35.0 | 135.0 | 31.0 | 93.0 | 5.0 | 142.0 | 2.0 | 6.0 | 97.0 | 14.0 | 39.0 | 150.0 | 156.0 | 0.404762 | 0.529851 | 0.735294 | 0.916667 | 0.380531 | 0.764706 | 0.652695 | 0.566879 | 0.755591 | 0.384615 | 0.679426 | 0.133333 | 0.171429 | 0.718519 | 0.451613 | 0.419355 | 0.579151 | 0.398977 | 26.888218 | 89.080708 | 86.922745 | 84.613725 | 91.411308 | 65.753129 | 51.035822 | 37.958567 | 33.750539 | 62.624083 | 93.223997 | 59.883470 | 76.046612 | 96.157168 | 46.331463 | 49.525248 | 18.277946 | 57.423392 | 39.792836 | 66.767372 | 91.519206 | 47.388865 | 46.245145 |
| 2273 | Murilo Bustamante | Orthodox | Yes | No | Yes | Yes | 2.0 | 6.0 | 2.0 | 0.0 | 7.183333 | 36.0 | 4.0 | 1.0 | 24.0 | 4.0 | 13.0 | 41.0 | 82.0 | 68.0 | 109.0 | 29.0 | 77.0 | 4.0 | 1.0 | 42.0 | 11.0 | 29.0 | 1.0 | 0.0 | 1.0 | 1.0 | 6.750000 | 24.0 | 15.0 | 37.0 | 30.0 | 34.0 | 12.0 | 76.0 | 177.0 | 132.0 | 233.0 | 3.0 | 118.0 | 18.0 | 41.0 | 118.0 | 43.0 | 16.0 | 3.0 | 94.0 | 3.0 | 4.0 | 88.0 | 9.0 | 4.0 | 101.0 | 101.0 | 0.206897 | 0.467532 | 1.000000 | 1.000000 | 0.571429 | 0.363636 | 0.448276 | 0.500000 | 0.623853 | 1.000000 | 0.796610 | 0.166667 | 0.097561 | 0.745763 | 0.209302 | 0.250000 | 0.570621 | 0.433476 | 76.542943 | 67.393181 | 65.105740 | 29.477773 | 49.633146 | 32.088908 | 15.645231 | 9.451877 | 22.227018 | 22.183858 | 49.482089 | 23.327579 | 23.068623 | 79.663212 | 35.261114 | 37.052223 | 23.327579 | 48.640483 | 37.634873 | 53.323263 | 33.448425 | 36.404834 | 33.707380 |
| 2278 | Pat Miletich | Orthodox | Yes | No | Yes | Yes | 1.0 | 6.0 | 3.0 | 0.0 | 14.166667 | 47.0 | 6.0 | 11.0 | 40.0 | 9.0 | 15.0 | 64.0 | 140.0 | 202.0 | 281.0 | 6.0 | 120.0 | 7.0 | 13.0 | 107.0 | 14.0 | 19.0 | 0.0 | 5.0 | 3.0 | 0.0 | 7.366667 | 27.0 | 14.0 | 11.0 | 21.0 | 10.0 | 21.0 | 52.0 | 143.0 | 145.0 | 245.0 | 11.0 | 114.0 | 16.0 | 13.0 | 86.0 | 13.0 | 44.0 | 6.0 | 87.0 | 2.0 | 2.0 | 65.0 | 3.0 | 23.0 | 91.0 | 100.0 | 1.000000 | 0.391667 | 0.857143 | 0.846154 | 0.373832 | 0.642857 | 0.789474 | 0.457143 | 0.718861 | 0.545455 | 0.763158 | 0.125000 | 0.153846 | 0.755814 | 0.230769 | 0.522727 | 0.636364 | 0.408163 | 62.580924 | 67.393181 | 74.126025 | 29.477773 | 68.580060 | 37.440656 | 19.896418 | 37.958567 | 32.455762 | 36.426413 | 52.244281 | 32.606819 | 50.107898 | 93.523316 | 50.841606 | 35.369012 | 18.277946 | 35.930082 | 30.513595 | 28.441951 | 80.427277 | 33.858438 | 33.470004 |
| 2282 | Kevin Randleman | Orthodox | Yes | No | Yes | Yes | 0.0 | 5.0 | 3.0 | 0.0 | 25.866667 | 43.0 | 6.0 | 1.0 | 14.0 | 8.0 | 28.0 | 50.0 | 110.0 | 153.0 | 222.0 | 9.0 | 103.0 | 6.0 | 1.0 | 46.0 | 10.0 | 54.0 | 1.0 | 1.0 | 6.0 | 0.0 | 2.983333 | 23.0 | 9.0 | 13.0 | 14.0 | 16.0 | 15.0 | 45.0 | 106.0 | 115.0 | 180.0 | 1.0 | 81.0 | 10.0 | 15.0 | 65.0 | 19.0 | 22.0 | 0.0 | 58.0 | 1.0 | 2.0 | 51.0 | 3.0 | 7.0 | 61.0 | 65.0 | 0.555556 | 0.417476 | 1.000000 | 1.000000 | 0.304348 | 0.800000 | 0.518519 | 0.454545 | 0.689189 | 0.000000 | 0.716049 | 0.100000 | 0.133333 | 0.784615 | 0.157895 | 0.318182 | 0.575472 | 0.361111 | 26.888218 | 62.127751 | 74.126025 | 29.477773 | 82.693138 | 35.800604 | 19.896418 | 9.451877 | 16.659473 | 33.987915 | 66.724212 | 27.384549 | 42.986621 | 90.198618 | 7.466552 | 25.334484 | 12.473025 | 35.930082 | 24.838153 | 28.441951 | 45.986189 | 24.126025 | 23.047044 |
105 rows × 100 columns
c_champions.describe()
| KD | TD | SUB.ATT | REV | CTRL | HEAD | BODY | LEG | DISTANCE | CLINCH | GROUND | SIG | TOT.SIG | STR | TOTAL.STR | TOTAL.TD | TOTAL.HEAD | TOTAL.BODY | TOTAL.LEG | TOTAL.DISTANCE | TOTAL.CLINCH | TOTAL.GROUND | OPP_KD | OPP_TD | OPP_SUB.ATT | OPP_REV | OPP_CTRL | OPP_HEAD | OPP_BODY | OPP_LEG | OPP_DISTANCE | OPP_CLINCH | OPP_GROUND | OPP_SIG | OPP_TOT.SIG | OPP_STR | OPP_TOTAL.STR | OPP_TOTAL.TD | OPP_TOTAL.HEAD | OPP_TOTAL.BODY | OPP_TOTAL.LEG | OPP_TOTAL.DISTANCE | OPP_TOTAL.CLINCH | OPP_TOTAL.GROUND | TD_DEF | HEAD_DEF | BODY_DEF | LEG_DEF | DISTANCE_DEF | CLINCH_DEF | GROUND_DEF | SIG_DEF | STR_DEF | TD_% | HEAD_% | BODY_% | LEG_% | DISTANCE_% | CLINCH_% | GROUND_% | SIG_% | STR_% | TD_DEF_% | HEAD_DEF_% | BODY_DEF_% | LEG_DEF_% | DISTANCE_DEF_% | CLINCH_DEF_% | GROUND_DEF_% | SIG_DEF_% | STR_DEF_% | KD_PERCENTILE | TD_PERCENTILE | SUB.ATT_PERCENTILE | REV_PERCENTILE | CTRL_PERCENTILE | HEAD_PERCENTILE | BODY_PERCENTILE | LEG_PERCENTILE | DISTANCE_PERCENTILE | CLINCH_PERCENTILE | GROUND_PERCENTILE | SIG_PERCENTILE | STR_PERCENTILE | STR_%_PERCENTILE | TD_DEF_PERCENTILE | HEAD_DEF_PERCENTILE | BODY_DEF_PERCENTILE | LEG_DEF_PERCENTILE | DISTANCE_DEF_PERCENTILE | CLINCH_DEF_PERCENTILE | GROUND_DEF_PERCENTILE | SIG_DEF_PERCENTILE | STR_DEF_PERCENTILE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 104.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 | 105.000000 |
| mean | 5.095238 | 21.266667 | 6.523810 | 1.647619 | 43.173333 | 502.019048 | 155.123810 | 124.000000 | 557.952381 | 105.285714 | 117.904762 | 781.142857 | 1642.685714 | 1117.323810 | 2015.828571 | 50.457143 | 1275.323810 | 214.466667 | 152.895238 | 1326.704762 | 147.400000 | 168.580952 | 2.847619 | 14.514286 | 4.180952 | 1.523810 | 29.469206 | 386.523810 | 128.561905 | 107.238095 | 474.095238 | 83.104762 | 65.123810 | 622.323810 | 1481.914286 | 890.666667 | 1780.542857 | 47.152381 | 1152.523810 | 191.552381 | 137.838095 | 1265.676190 | 121.314286 | 94.923810 | 32.638095 | 766.000000 | 62.990476 | 30.600000 | 791.580952 | 38.209524 | 29.800000 | 859.590476 | 889.876190 | 0.470467 | 0.404081 | 0.752657 | 0.842307 | 0.413881 | 0.714435 | 0.692284 | 0.485006 | 0.572208 | 0.662773 | 0.656814 | 0.299603 | 0.201431 | 0.625127 | 0.309558 | 0.309731 | 0.568168 | 0.481506 | 81.512218 | 79.423516 | 75.471566 | 62.787060 | 82.224962 | 86.612409 | 83.800070 | 81.137349 | 82.934007 | 84.387652 | 85.805948 | 86.254187 | 87.157449 | 60.918044 | 79.541073 | 83.239616 | 78.732145 | 79.246152 | 82.739585 | 78.587048 | 70.326572 | 83.157819 | 83.047249 |
| std | 4.093907 | 19.244513 | 6.342917 | 2.014281 | 33.787510 | 337.013324 | 116.603428 | 103.537247 | 426.853726 | 79.759821 | 92.796564 | 502.187148 | 1096.090582 | 636.510371 | 1219.192462 | 45.315566 | 878.010135 | 162.951271 | 131.673323 | 994.435131 | 109.010285 | 129.461145 | 2.691800 | 12.000893 | 4.652917 | 2.057329 | 23.436535 | 257.322758 | 86.901350 | 79.699499 | 339.153940 | 61.365068 | 55.461819 | 393.483567 | 974.864351 | 507.084553 | 1081.990189 | 35.009005 | 772.717123 | 134.888414 | 103.124843 | 897.691853 | 89.748400 | 81.327058 | 26.520719 | 531.706433 | 52.441450 | 27.091937 | 569.087474 | 31.031527 | 29.272066 | 594.604922 | 605.379231 | 0.186016 | 0.078132 | 0.098097 | 0.080815 | 0.081676 | 0.088107 | 0.091579 | 0.075536 | 0.083090 | 0.181445 | 0.078363 | 0.102195 | 0.086620 | 0.063756 | 0.095455 | 0.127376 | 0.063929 | 0.083670 | 21.331003 | 21.598285 | 24.066553 | 28.237597 | 18.175600 | 16.748847 | 19.942017 | 21.412176 | 21.155646 | 16.903322 | 15.598349 | 17.681014 | 15.854936 | 22.956788 | 22.193001 | 19.244928 | 24.508144 | 23.846069 | 20.105209 | 22.284079 | 24.445873 | 19.570818 | 19.770556 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.966667 | 11.000000 | 4.000000 | 1.000000 | 11.000000 | 4.000000 | 3.000000 | 23.000000 | 77.000000 | 68.000000 | 109.000000 | 0.000000 | 64.000000 | 4.000000 | 1.000000 | 42.000000 | 10.000000 | 8.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.016667 | 21.000000 | 9.000000 | 5.000000 | 14.000000 | 3.000000 | 0.000000 | 45.000000 | 106.000000 | 52.000000 | 146.000000 | 1.000000 | 81.000000 | 10.000000 | 6.000000 | 65.000000 | 6.000000 | 1.000000 | 0.000000 | 58.000000 | 1.000000 | 0.000000 | 51.000000 | 1.000000 | 1.000000 | 61.000000 | 65.000000 | 0.000000 | 0.171875 | 0.561170 | 0.673077 | 0.200000 | 0.363636 | 0.375000 | 0.298701 | 0.388996 | 0.000000 | 0.447059 | 0.052632 | 0.000000 | 0.433333 | 0.083333 | 0.052632 | 0.389796 | 0.245614 | 26.888218 | 11.588261 | 21.255934 | 29.477773 | 24.298662 | 13.875701 | 15.645231 | 9.451877 | 14.134657 | 22.183858 | 29.261977 | 15.062581 | 23.068623 | 11.010363 | 7.466552 | 25.334484 | 12.473025 | 9.387139 | 24.838153 | 15.580492 | 16.508416 | 24.126025 | 23.047044 |
| 25% | 2.000000 | 6.000000 | 2.000000 | 0.000000 | 16.116667 | 263.000000 | 81.000000 | 49.000000 | 220.000000 | 51.000000 | 49.000000 | 400.000000 | 837.000000 | 593.000000 | 1076.000000 | 13.000000 | 696.000000 | 97.000000 | 55.000000 | 565.000000 | 66.000000 | 68.000000 | 1.000000 | 6.000000 | 1.000000 | 0.000000 | 10.550000 | 192.000000 | 60.000000 | 45.000000 | 203.000000 | 37.000000 | 23.000000 | 301.000000 | 820.000000 | 453.000000 | 990.000000 | 19.000000 | 636.000000 | 84.000000 | 54.000000 | 507.000000 | 53.000000 | 35.000000 | 13.000000 | 372.000000 | 24.000000 | 9.000000 | 335.000000 | 14.000000 | 9.000000 | 413.000000 | 434.000000 | 0.349457 | 0.346875 | 0.686275 | 0.780488 | 0.351759 | 0.663717 | 0.635983 | 0.431452 | 0.514248 | 0.580645 | 0.602083 | 0.239437 | 0.148718 | 0.585253 | 0.238806 | 0.227642 | 0.534537 | 0.427562 | 76.542943 | 67.393181 | 65.105740 | 29.477773 | 71.773845 | 83.491584 | 81.722054 | 75.701338 | 75.463962 | 79.952525 | 78.830384 | 82.024169 | 82.563660 | 43.652850 | 71.126457 | 77.492447 | 69.917997 | 67.522659 | 74.665516 | 66.767372 | 53.301683 | 77.255071 | 77.211912 |
| 50% | 4.000000 | 16.000000 | 5.000000 | 1.000000 | 33.250000 | 454.000000 | 132.000000 | 99.000000 | 509.000000 | 89.000000 | 101.000000 | 738.000000 | 1483.000000 | 1064.000000 | 1881.000000 | 42.000000 | 1135.000000 | 186.000000 | 123.000000 | 1208.000000 | 124.000000 | 142.000000 | 2.000000 | 12.000000 | 3.000000 | 1.000000 | 23.983333 | 348.000000 | 113.000000 | 93.000000 | 401.000000 | 73.000000 | 49.000000 | 588.000000 | 1369.000000 | 912.000000 | 1679.000000 | 40.000000 | 1062.000000 | 161.000000 | 124.000000 | 1163.000000 | 105.000000 | 68.000000 | 27.000000 | 690.000000 | 57.000000 | 25.000000 | 704.000000 | 31.000000 | 18.000000 | 781.000000 | 816.000000 | 0.443461 | 0.408421 | 0.735294 | 0.841667 | 0.413669 | 0.719178 | 0.702128 | 0.480064 | 0.577503 | 0.678571 | 0.654562 | 0.303571 | 0.195122 | 0.620722 | 0.318182 | 0.300000 | 0.570755 | 0.483427 | 89.404402 | 88.044886 | 83.707380 | 69.486405 | 88.044886 | 92.490289 | 91.152352 | 90.030211 | 92.274493 | 90.914976 | 92.425550 | 93.180837 | 93.698748 | 64.853195 | 88.713854 | 91.454467 | 90.807078 | 90.526543 | 91.303410 | 87.030643 | 74.492879 | 91.713423 | 91.670263 |
| 75% | 8.000000 | 30.000000 | 10.000000 | 3.000000 | 60.000000 | 668.000000 | 207.000000 | 198.000000 | 755.000000 | 141.000000 | 152.000000 | 999.000000 | 2154.000000 | 1521.000000 | 2716.000000 | 75.000000 | 1665.000000 | 283.000000 | 249.000000 | 1740.000000 | 196.000000 | 218.000000 | 5.000000 | 19.000000 | 6.000000 | 2.000000 | 43.783333 | 519.000000 | 196.000000 | 152.000000 | 633.000000 | 116.000000 | 92.000000 | 822.000000 | 1920.000000 | 1158.000000 | 2373.000000 | 66.000000 | 1504.000000 | 276.000000 | 195.000000 | 1641.000000 | 159.000000 | 135.000000 | 46.000000 | 1021.000000 | 94.000000 | 43.000000 | 1081.000000 | 49.000000 | 40.000000 | 1133.000000 | 1195.000000 | 0.549903 | 0.458112 | 0.820433 | 0.903226 | 0.463583 | 0.779221 | 0.753425 | 0.532674 | 0.623351 | 0.777778 | 0.711343 | 0.367232 | 0.257812 | 0.670838 | 0.354839 | 0.375661 | 0.618056 | 0.545287 | 96.978852 | 95.813552 | 94.691411 | 91.842900 | 96.719896 | 97.626241 | 96.763056 | 97.496763 | 96.806215 | 96.223565 | 97.000432 | 97.022011 | 97.475183 | 79.576857 | 96.266724 | 96.439361 | 96.892533 | 96.892533 | 96.460941 | 94.907208 | 92.015537 | 96.374622 | 96.460941 |
| max | 18.000000 | 90.000000 | 40.000000 | 11.000000 | 162.116667 | 2117.000000 | 723.000000 | 508.000000 | 2709.000000 | 359.000000 | 461.000000 | 3122.000000 | 6586.000000 | 3366.000000 | 6859.000000 | 232.000000 | 5254.000000 | 964.000000 | 630.000000 | 6034.000000 | 474.000000 | 656.000000 | 12.000000 | 74.000000 | 28.000000 | 9.000000 | 125.066667 | 1310.000000 | 454.000000 | 486.000000 | 1930.000000 | 293.000000 | 220.000000 | 2086.000000 | 5151.000000 | 2460.000000 | 5276.000000 | 165.000000 | 4005.000000 | 770.000000 | 611.000000 | 4894.000000 | 437.000000 | 334.000000 | 126.000000 | 2695.000000 | 316.000000 | 128.000000 | 2964.000000 | 144.000000 | 139.000000 | 3065.000000 | 3085.000000 | 1.000000 | 0.652361 | 1.000000 | 1.000000 | 0.620968 | 0.933333 | 0.870056 | 0.719595 | 0.824593 | 1.000000 | 0.812500 | 0.519481 | 0.403846 | 0.784615 | 0.687500 | 1.000000 | 0.730727 | 0.643836 | 99.935261 | 100.000000 | 99.956841 | 99.892102 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 99.956841 | 100.000000 | 100.000000 | 100.000000 | 98.402418 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 99.956841 | 99.870522 | 100.000000 | 100.000000 |
# List of columns to calculate means
columns_of_interest = [
"KD_PERCENTILE", "TD_PERCENTILE", "SUB.ATT_PERCENTILE", "REV_PERCENTILE", "CTRL_PERCENTILE",
"HEAD_PERCENTILE", "BODY_PERCENTILE", "LEG_PERCENTILE", "DISTANCE_PERCENTILE", "CLINCH_PERCENTILE",
"GROUND_PERCENTILE", "SIG_PERCENTILE", "STR_PERCENTILE", "STR_%_PERCENTILE", "TD_DEF_PERCENTILE",
"HEAD_DEF_PERCENTILE", "BODY_DEF_PERCENTILE", "LEG_DEF_PERCENTILE", "DISTANCE_DEF_PERCENTILE",
"CLINCH_DEF_PERCENTILE", "GROUND_DEF_PERCENTILE", "SIG_DEF_PERCENTILE", "STR_DEF_PERCENTILE"
]
# Calculate the mean for each of the specified columns
means1 = c_champions[columns_of_interest].mean()
# Rank the means from highest to lowest and plot again
sorted_means1 = means1.sort_values(ascending=False)
# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_means1.plot(kind='bar', color='gold')
plt.title('Mean Percentiles across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90) # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()
# Display max rows
pd.set_option('display.max_rows', None)
print(sorted_means1)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)
STR_PERCENTILE 87.157449 HEAD_PERCENTILE 86.612409 SIG_PERCENTILE 86.254187 GROUND_PERCENTILE 85.805948 CLINCH_PERCENTILE 84.387652 BODY_PERCENTILE 83.800070 HEAD_DEF_PERCENTILE 83.239616 SIG_DEF_PERCENTILE 83.157819 STR_DEF_PERCENTILE 83.047249 DISTANCE_PERCENTILE 82.934007 DISTANCE_DEF_PERCENTILE 82.739585 CTRL_PERCENTILE 82.224962 KD_PERCENTILE 81.512218 LEG_PERCENTILE 81.137349 TD_DEF_PERCENTILE 79.541073 TD_PERCENTILE 79.423516 LEG_DEF_PERCENTILE 79.246152 BODY_DEF_PERCENTILE 78.732145 CLINCH_DEF_PERCENTILE 78.587048 SUB.ATT_PERCENTILE 75.471566 GROUND_DEF_PERCENTILE 70.326572 REV_PERCENTILE 62.787060 STR_%_PERCENTILE 60.918044 dtype: float64
# List of columns to calculate means
columns_of_interest = [
"TD_%", 'HEAD_%', 'BODY_%', 'LEG_%', 'DISTANCE_%', 'CLINCH_%', 'GROUND_%', 'SIG_%', 'STR_%', 'TD_DEF_%',
'HEAD_DEF_%', 'BODY_DEF_%', 'LEG_DEF_%', 'DISTANCE_DEF_%', 'CLINCH_DEF_%', 'GROUND_DEF_%', 'SIG_DEF_%', 'STR_DEF_%'
]
# Calculate the mean for each of the specified columns
meansA = c_champions[columns_of_interest].mean()
# Rank the means from highest to lowest and plot again
sorted_meansA = meansA.sort_values(ascending=False)
# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_meansA.plot(kind='bar', color='rebeccapurple')
plt.title('Mean Percentiles across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90) # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()
# Display max rows
pd.set_option('display.max_rows', None)
print(sorted_meansA)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)
LEG_% 0.842307 BODY_% 0.752657 CLINCH_% 0.714435 GROUND_% 0.692284 TD_DEF_% 0.662773 HEAD_DEF_% 0.656814 DISTANCE_DEF_% 0.625127 STR_% 0.572208 SIG_DEF_% 0.568168 SIG_% 0.485006 STR_DEF_% 0.481506 TD_% 0.470467 DISTANCE_% 0.413881 HEAD_% 0.404081 GROUND_DEF_% 0.309731 CLINCH_DEF_% 0.309558 BODY_DEF_% 0.299603 LEG_DEF_% 0.201431 dtype: float64
# Challengers
c_challengers = career[career['EVER_CHALLENGER'] == 'Yes']
c_challengers
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | KD | TD | SUB.ATT | REV | CTRL | HEAD | BODY | LEG | DISTANCE | CLINCH | GROUND | SIG | TOT.SIG | STR | TOTAL.STR | TOTAL.TD | TOTAL.HEAD | TOTAL.BODY | TOTAL.LEG | TOTAL.DISTANCE | TOTAL.CLINCH | TOTAL.GROUND | OPP_KD | OPP_TD | OPP_SUB.ATT | OPP_REV | OPP_CTRL | OPP_HEAD | OPP_BODY | OPP_LEG | OPP_DISTANCE | OPP_CLINCH | OPP_GROUND | OPP_SIG | OPP_TOT.SIG | OPP_STR | OPP_TOTAL.STR | OPP_TOTAL.TD | OPP_TOTAL.HEAD | OPP_TOTAL.BODY | OPP_TOTAL.LEG | OPP_TOTAL.DISTANCE | OPP_TOTAL.CLINCH | OPP_TOTAL.GROUND | TD_DEF | HEAD_DEF | BODY_DEF | LEG_DEF | DISTANCE_DEF | CLINCH_DEF | GROUND_DEF | SIG_DEF | STR_DEF | TD_% | HEAD_% | BODY_% | LEG_% | DISTANCE_% | CLINCH_% | GROUND_% | SIG_% | STR_% | TD_DEF_% | HEAD_DEF_% | BODY_DEF_% | LEG_DEF_% | DISTANCE_DEF_% | CLINCH_DEF_% | GROUND_DEF_% | SIG_DEF_% | STR_DEF_% | KD_PERCENTILE | TD_PERCENTILE | SUB.ATT_PERCENTILE | REV_PERCENTILE | CTRL_PERCENTILE | HEAD_PERCENTILE | BODY_PERCENTILE | LEG_PERCENTILE | DISTANCE_PERCENTILE | CLINCH_PERCENTILE | GROUND_PERCENTILE | SIG_PERCENTILE | STR_PERCENTILE | STR_%_PERCENTILE | TD_DEF_PERCENTILE | HEAD_DEF_PERCENTILE | BODY_DEF_PERCENTILE | LEG_DEF_PERCENTILE | DISTANCE_DEF_PERCENTILE | CLINCH_DEF_PERCENTILE | GROUND_DEF_PERCENTILE | SIG_DEF_PERCENTILE | STR_DEF_PERCENTILE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5 | Dustin Poirier | Southpaw | No | Yes | Yes | Yes | 14.0 | 28.0 | 24.0 | 4.0 | 69.400000 | 1343.0 | 158.0 | 177.0 | 1262.0 | 254.0 | 162.0 | 1678.0 | 3284.0 | 2010.0 | 3654.0 | 77.0 | 2850.0 | 219.0 | 215.0 | 2644.0 | 387.0 | 253.0 | 3.0 | 29.0 | 28.0 | 3.0 | 58.800000 | 864.0 | 293.0 | 184.0 | 1032.0 | 186.0 | 123.0 | 1341.0 | 2881.0 | 1780.0 | 3372.0 | 76.0 | 2257.0 | 404.0 | 220.0 | 2464.0 | 262.0 | 155.0 | 47.0 | 1393.0 | 111.0 | 36.0 | 1432.0 | 76.0 | 32.0 | 1540.0 | 1592.0 | 0.363636 | 0.471228 | 0.721461 | 0.823256 | 0.477307 | 0.656331 | 0.640316 | 0.510962 | 0.550082 | 0.618421 | 0.617191 | 0.274752 | 0.163636 | 0.581169 | 0.290076 | 0.206452 | 0.534537 | 0.472123 | 99.719465 | 95.317221 | 99.568407 | 95.684074 | 97.583082 | 99.913681 | 93.828226 | 96.935693 | 99.525248 | 99.568407 | 97.496763 | 99.697885 | 99.482089 | 56.174439 | 96.568839 | 98.662063 | 98.036254 | 95.274061 | 98.662063 | 98.273630 | 87.591713 | 98.748382 | 98.748382 |
| 6 | Gilbert Burns | Orthodox | No | No | Yes | No | 4.0 | 40.0 | 9.0 | 0.0 | 67.066667 | 526.0 | 187.0 | 169.0 | 661.0 | 83.0 | 138.0 | 882.0 | 1818.0 | 1275.0 | 2265.0 | 103.0 | 1361.0 | 243.0 | 214.0 | 1521.0 | 124.0 | 173.0 | 8.0 | 12.0 | 1.0 | 0.0 | 25.850000 | 587.0 | 181.0 | 148.0 | 770.0 | 75.0 | 71.0 | 916.0 | 1997.0 | 1209.0 | 2324.0 | 24.0 | 1553.0 | 276.0 | 168.0 | 1795.0 | 101.0 | 101.0 | 12.0 | 966.0 | 95.0 | 20.0 | 1025.0 | 26.0 | 30.0 | 1081.0 | 1115.0 | 0.388350 | 0.386481 | 0.769547 | 0.789720 | 0.434583 | 0.669355 | 0.797688 | 0.485149 | 0.562914 | 0.500000 | 0.622022 | 0.344203 | 0.119048 | 0.571031 | 0.257426 | 0.297030 | 0.541312 | 0.479776 | 89.404402 | 98.165732 | 93.202417 | 29.477773 | 97.453604 | 94.561934 | 95.748813 | 96.396202 | 95.381959 | 90.008632 | 95.964609 | 95.770393 | 95.684074 | 60.405872 | 69.227449 | 95.943030 | 97.065170 | 85.973241 | 96.158826 | 82.908934 | 86.383254 | 95.986189 | 95.856711 |
| 17 | Rafael Dos Anjos | Southpaw | Yes | No | Yes | Yes | 7.0 | 68.0 | 20.0 | 3.0 | 136.750000 | 1061.0 | 433.0 | 324.0 | 1220.0 | 321.0 | 277.0 | 1818.0 | 3858.0 | 2587.0 | 4723.0 | 191.0 | 2880.0 | 588.0 | 390.0 | 3046.0 | 407.0 | 405.0 | 2.0 | 74.0 | 2.0 | 2.0 | 125.066667 | 1090.0 | 349.0 | 235.0 | 1321.0 | 235.0 | 118.0 | 1674.0 | 4225.0 | 2460.0 | 5110.0 | 165.0 | 3438.0 | 527.0 | 260.0 | 3726.0 | 324.0 | 175.0 | 91.0 | 2348.0 | 178.0 | 25.0 | 2405.0 | 89.0 | 57.0 | 2551.0 | 2650.0 | 0.356021 | 0.368403 | 0.736395 | 0.830769 | 0.400525 | 0.788698 | 0.683951 | 0.471229 | 0.547745 | 0.551515 | 0.682955 | 0.337761 | 0.096154 | 0.645464 | 0.274691 | 0.325714 | 0.603787 | 0.518591 | 95.899871 | 99.633146 | 99.179974 | 91.842900 | 99.870522 | 99.697885 | 99.741044 | 99.482089 | 99.438930 | 99.827363 | 99.568407 | 99.870522 | 99.913681 | 55.181347 | 99.741044 | 99.870522 | 99.784204 | 90.526543 | 99.870522 | 99.093656 | 96.331463 | 99.870522 | 99.913681 |
| 22 | Petr Yan | Switch | Yes | Yes | Yes | Yes | 10.0 | 25.0 | 2.0 | 2.0 | 32.866667 | 758.0 | 231.0 | 126.0 | 806.0 | 122.0 | 187.0 | 1115.0 | 2083.0 | 1438.0 | 2437.0 | 49.0 | 1665.0 | 284.0 | 134.0 | 1700.0 | 154.0 | 229.0 | 1.0 | 19.0 | 0.0 | 2.0 | 25.266667 | 592.0 | 216.0 | 138.0 | 840.0 | 75.0 | 31.0 | 946.0 | 2291.0 | 1103.0 | 2473.0 | 128.0 | 1784.0 | 329.0 | 178.0 | 2143.0 | 106.0 | 42.0 | 109.0 | 1192.0 | 113.0 | 40.0 | 1303.0 | 31.0 | 11.0 | 1345.0 | 1370.0 | 0.510204 | 0.455255 | 0.813380 | 0.940299 | 0.474118 | 0.792208 | 0.816594 | 0.535286 | 0.590070 | 0.851562 | 0.668161 | 0.343465 | 0.224719 | 0.608026 | 0.292453 | 0.261905 | 0.587080 | 0.553983 | 98.511006 | 94.238239 | 65.105740 | 84.613725 | 87.915408 | 98.446267 | 97.561502 | 93.396634 | 97.237808 | 94.885628 | 98.359948 | 97.928356 | 97.000432 | 69.689119 | 99.913681 | 97.755719 | 98.165732 | 96.288304 | 98.144152 | 87.030643 | 58.761329 | 97.798878 | 97.755719 |
| 26 | Marlon Vera | Switch | No | No | Yes | No | 12.0 | 11.0 | 18.0 | 1.0 | 30.366667 | 730.0 | 279.0 | 321.0 | 1041.0 | 138.0 | 151.0 | 1330.0 | 2696.0 | 1533.0 | 2937.0 | 28.0 | 1859.0 | 451.0 | 386.0 | 2294.0 | 175.0 | 227.0 | 0.0 | 22.0 | 0.0 | 2.0 | 61.283333 | 1050.0 | 375.0 | 268.0 | 1417.0 | 112.0 | 164.0 | 1693.0 | 3391.0 | 2098.0 | 3874.0 | 74.0 | 2552.0 | 518.0 | 321.0 | 2998.0 | 164.0 | 229.0 | 52.0 | 1502.0 | 143.0 | 53.0 | 1581.0 | 52.0 | 65.0 | 1698.0 | 1776.0 | 0.392857 | 0.392684 | 0.618625 | 0.831606 | 0.453793 | 0.788571 | 0.665198 | 0.493323 | 0.521961 | 0.702703 | 0.588558 | 0.276062 | 0.165109 | 0.527352 | 0.317073 | 0.283843 | 0.500737 | 0.458441 | 99.244713 | 81.031506 | 98.964178 | 69.486405 | 85.757445 | 98.230470 | 98.705222 | 99.438930 | 98.791541 | 96.029348 | 96.914113 | 98.921019 | 97.626241 | 46.718480 | 97.604661 | 99.050496 | 99.438930 | 98.338369 | 99.093656 | 95.899871 | 97.022011 | 99.179974 | 99.223133 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2277 | Hayato Sakurai | Orthodox | No | No | Yes | No | 1.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 4.0 | 0.0 | 1.0 | 5.0 | 0.0 | 0.0 | 5.0 | 12.0 | 14.0 | 22.0 | 0.0 | 11.0 | 0.0 | 1.0 | 12.0 | 0.0 | 0.0 | 0.0 | 9.0 | 0.0 | 0.0 | 15.866667 | 34.0 | 2.0 | 0.0 | 8.0 | 1.0 | 27.0 | 36.0 | 58.0 | 112.0 | 137.0 | 11.0 | 56.0 | 2.0 | 0.0 | 12.0 | 1.0 | 45.0 | 2.0 | 22.0 | 0.0 | 0.0 | 4.0 | 0.0 | 18.0 | 22.0 | 25.0 | NaN | 0.363636 | NaN | 1.000000 | 0.416667 | NaN | NaN | 0.416667 | 0.636364 | 0.181818 | 0.392857 | 0.000000 | NaN | 0.333333 | 0.000000 | 0.400000 | 0.379310 | 0.182482 | 62.580924 | 11.588261 | 21.255934 | 29.477773 | 2.546396 | 7.509711 | 2.740613 | 9.451877 | 8.782909 | 4.294346 | 8.610272 | 5.114372 | 6.409150 | 82.059585 | 28.226155 | 11.221407 | 4.661200 | 9.387139 | 4.380665 | 5.955978 | 74.492879 | 10.250324 | 9.753992 |
| 2278 | Pat Miletich | Orthodox | Yes | No | Yes | Yes | 1.0 | 6.0 | 3.0 | 0.0 | 14.166667 | 47.0 | 6.0 | 11.0 | 40.0 | 9.0 | 15.0 | 64.0 | 140.0 | 202.0 | 281.0 | 6.0 | 120.0 | 7.0 | 13.0 | 107.0 | 14.0 | 19.0 | 0.0 | 5.0 | 3.0 | 0.0 | 7.366667 | 27.0 | 14.0 | 11.0 | 21.0 | 10.0 | 21.0 | 52.0 | 143.0 | 145.0 | 245.0 | 11.0 | 114.0 | 16.0 | 13.0 | 86.0 | 13.0 | 44.0 | 6.0 | 87.0 | 2.0 | 2.0 | 65.0 | 3.0 | 23.0 | 91.0 | 100.0 | 1.000000 | 0.391667 | 0.857143 | 0.846154 | 0.373832 | 0.642857 | 0.789474 | 0.457143 | 0.718861 | 0.545455 | 0.763158 | 0.125000 | 0.153846 | 0.755814 | 0.230769 | 0.522727 | 0.636364 | 0.408163 | 62.580924 | 67.393181 | 74.126025 | 29.477773 | 68.580060 | 37.440656 | 19.896418 | 37.958567 | 32.455762 | 36.426413 | 52.244281 | 32.606819 | 50.107898 | 93.523316 | 50.841606 | 35.369012 | 18.277946 | 35.930082 | 30.513595 | 28.441951 | 80.427277 | 33.858438 | 33.470004 |
| 2282 | Kevin Randleman | Orthodox | Yes | No | Yes | Yes | 0.0 | 5.0 | 3.0 | 0.0 | 25.866667 | 43.0 | 6.0 | 1.0 | 14.0 | 8.0 | 28.0 | 50.0 | 110.0 | 153.0 | 222.0 | 9.0 | 103.0 | 6.0 | 1.0 | 46.0 | 10.0 | 54.0 | 1.0 | 1.0 | 6.0 | 0.0 | 2.983333 | 23.0 | 9.0 | 13.0 | 14.0 | 16.0 | 15.0 | 45.0 | 106.0 | 115.0 | 180.0 | 1.0 | 81.0 | 10.0 | 15.0 | 65.0 | 19.0 | 22.0 | 0.0 | 58.0 | 1.0 | 2.0 | 51.0 | 3.0 | 7.0 | 61.0 | 65.0 | 0.555556 | 0.417476 | 1.000000 | 1.000000 | 0.304348 | 0.800000 | 0.518519 | 0.454545 | 0.689189 | 0.000000 | 0.716049 | 0.100000 | 0.133333 | 0.784615 | 0.157895 | 0.318182 | 0.575472 | 0.361111 | 26.888218 | 62.127751 | 74.126025 | 29.477773 | 82.693138 | 35.800604 | 19.896418 | 9.451877 | 16.659473 | 33.987915 | 66.724212 | 27.384549 | 42.986621 | 90.198618 | 7.466552 | 25.334484 | 12.473025 | 35.930082 | 24.838153 | 28.441951 | 45.986189 | 24.126025 | 23.047044 |
| 2289 | Yuki Kondo | Southpaw | No | No | Yes | No | 2.0 | 0.0 | 0.0 | 2.0 | 4.316667 | 29.0 | 7.0 | 0.0 | 12.0 | 5.0 | 19.0 | 36.0 | 61.0 | 142.0 | 172.0 | 2.0 | 48.0 | 13.0 | 0.0 | 23.0 | 7.0 | 31.0 | 0.0 | 7.0 | 2.0 | 1.0 | 22.266667 | 42.0 | 5.0 | 0.0 | 2.0 | 1.0 | 44.0 | 47.0 | 93.0 | 174.0 | 241.0 | 13.0 | 86.0 | 7.0 | 0.0 | 8.0 | 1.0 | 84.0 | 6.0 | 44.0 | 2.0 | 0.0 | 6.0 | 0.0 | 40.0 | 46.0 | 67.0 | 0.000000 | 0.604167 | 0.538462 | NaN | 0.521739 | 0.714286 | 0.612903 | 0.590164 | 0.825581 | 0.461538 | 0.511628 | 0.285714 | NaN | 0.750000 | 0.000000 | 0.476190 | 0.494624 | 0.278008 | 76.542943 | 11.588261 | 21.255934 | 84.613725 | 37.311178 | 27.794562 | 22.183858 | 3.366422 | 14.954683 | 25.312905 | 56.927061 | 21.104877 | 41.066034 | 98.445596 | 50.841606 | 20.263271 | 18.277946 | 9.387139 | 5.869659 | 5.955978 | 92.015537 | 18.990073 | 23.953388 |
| 2296 | Kenichi Yamamoto | Orthodox | No | No | Yes | No | 0.0 | 0.0 | 0.0 | 0.0 | 0.016667 | 0.0 | 2.0 | 1.0 | 2.0 | 1.0 | 0.0 | 3.0 | 7.0 | 3.0 | 7.0 | 4.0 | 3.0 | 2.0 | 2.0 | 6.0 | 1.0 | 0.0 | 0.0 | 3.0 | 2.0 | 0.0 | 4.400000 | 15.0 | 0.0 | 1.0 | 5.0 | 1.0 | 10.0 | 16.0 | 28.0 | 31.0 | 43.0 | 3.0 | 25.0 | 0.0 | 3.0 | 15.0 | 1.0 | 12.0 | 0.0 | 10.0 | 0.0 | 2.0 | 10.0 | 0.0 | 2.0 | 12.0 | 12.0 | 0.000000 | 0.000000 | 1.000000 | 0.500000 | 0.333333 | 1.000000 | NaN | 0.428571 | 0.428571 | 0.000000 | 0.400000 | NaN | 0.666667 | 0.666667 | 0.000000 | 0.166667 | 0.428571 | 0.279070 | 26.888218 | 11.588261 | 21.255934 | 29.477773 | 5.308589 | 1.316357 | 10.703496 | 9.451877 | 4.984894 | 11.005611 | 8.610272 | 3.582218 | 2.028485 | 18.588083 | 7.466552 | 5.157531 | 4.661200 | 35.930082 | 8.178679 | 5.955978 | 22.874407 | 5.567544 | 4.661200 |
221 rows × 100 columns
c_challengers.describe()
| KD | TD | SUB.ATT | REV | CTRL | HEAD | BODY | LEG | DISTANCE | CLINCH | GROUND | SIG | TOT.SIG | STR | TOTAL.STR | TOTAL.TD | TOTAL.HEAD | TOTAL.BODY | TOTAL.LEG | TOTAL.DISTANCE | TOTAL.CLINCH | TOTAL.GROUND | OPP_KD | OPP_TD | OPP_SUB.ATT | OPP_REV | OPP_CTRL | OPP_HEAD | OPP_BODY | OPP_LEG | OPP_DISTANCE | OPP_CLINCH | OPP_GROUND | OPP_SIG | OPP_TOT.SIG | OPP_STR | OPP_TOTAL.STR | OPP_TOTAL.TD | OPP_TOTAL.HEAD | OPP_TOTAL.BODY | OPP_TOTAL.LEG | OPP_TOTAL.DISTANCE | OPP_TOTAL.CLINCH | OPP_TOTAL.GROUND | TD_DEF | HEAD_DEF | BODY_DEF | LEG_DEF | DISTANCE_DEF | CLINCH_DEF | GROUND_DEF | SIG_DEF | STR_DEF | TD_% | HEAD_% | BODY_% | LEG_% | DISTANCE_% | CLINCH_% | GROUND_% | SIG_% | STR_% | TD_DEF_% | HEAD_DEF_% | BODY_DEF_% | LEG_DEF_% | DISTANCE_DEF_% | CLINCH_DEF_% | GROUND_DEF_% | SIG_DEF_% | STR_DEF_% | KD_PERCENTILE | TD_PERCENTILE | SUB.ATT_PERCENTILE | REV_PERCENTILE | CTRL_PERCENTILE | HEAD_PERCENTILE | BODY_PERCENTILE | LEG_PERCENTILE | DISTANCE_PERCENTILE | CLINCH_PERCENTILE | GROUND_PERCENTILE | SIG_PERCENTILE | STR_PERCENTILE | STR_%_PERCENTILE | TD_DEF_PERCENTILE | HEAD_DEF_PERCENTILE | BODY_DEF_PERCENTILE | LEG_DEF_PERCENTILE | DISTANCE_DEF_PERCENTILE | CLINCH_DEF_PERCENTILE | GROUND_DEF_PERCENTILE | SIG_DEF_PERCENTILE | STR_DEF_PERCENTILE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.00000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 219.000000 | 221.000000 | 220.000000 | 220.000000 | 221.000000 | 220.000000 | 219.000000 | 221.000000 | 221.000000 | 219.000000 | 221.000000 | 220.000000 | 219.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 | 221.000000 |
| mean | 4.357466 | 17.755656 | 6.004525 | 1.633484 | 37.396908 | 409.457014 | 126.452489 | 100.502262 | 454.615385 | 84.561086 | 97.235294 | 636.411765 | 1395.570136 | 933.176471 | 1727.380090 | 44.986425 | 1092.303167 | 178.153846 | 125.113122 | 1134.601810 | 119.782805 | 141.18552 | 2.841629 | 14.158371 | 4.316742 | 1.475113 | 29.146531 | 357.524887 | 114.104072 | 99.945701 | 429.285068 | 75.185520 | 67.104072 | 571.574661 | 1330.457014 | 828.746606 | 1617.484163 | 41.855204 | 1036.511312 | 168.199095 | 125.746606 | 1123.583710 | 109.755656 | 97.117647 | 27.696833 | 678.986425 | 54.095023 | 25.800905 | 694.298643 | 34.570136 | 30.013575 | 758.882353 | 788.737557 | 0.416920 | 0.380254 | 0.727326 | 0.817924 | 0.389120 | 0.706195 | 0.688538 | 0.461697 | 0.556207 | 0.625632 | 0.639834 | 0.293381 | 0.194047 | 0.618785 | 0.309191 | 0.296140 | 0.554091 | 0.462211 | 75.908053 | 75.432423 | 71.692702 | 62.963010 | 78.282105 | 81.138135 | 78.360612 | 76.493828 | 77.663619 | 79.283947 | 81.194476 | 80.868243 | 82.380380 | 55.675548 | 76.084498 | 79.846873 | 75.243381 | 76.238973 | 78.909477 | 76.950613 | 71.705494 | 79.733994 | 79.824805 |
| std | 4.040005 | 17.143619 | 6.202271 | 1.967130 | 31.340069 | 298.931269 | 103.238355 | 91.737800 | 377.156818 | 69.719386 | 83.281553 | 451.899061 | 991.135497 | 592.667062 | 1121.197055 | 42.757613 | 790.989359 | 144.934135 | 115.537561 | 892.061145 | 95.702274 | 118.61928 | 2.633229 | 11.383293 | 4.755775 | 2.037281 | 21.607451 | 242.774878 | 82.262899 | 77.229273 | 326.431832 | 56.364858 | 52.632716 | 376.878531 | 903.374484 | 476.747378 | 998.824571 | 31.070987 | 711.911119 | 125.797443 | 97.482208 | 831.772187 | 81.688455 | 78.399992 | 23.115588 | 483.554448 | 47.419453 | 23.360732 | 517.423021 | 27.645661 | 29.548801 | 540.897524 | 550.802599 | 0.183160 | 0.084841 | 0.113222 | 0.094052 | 0.084171 | 0.100744 | 0.101691 | 0.078183 | 0.094823 | 0.188878 | 0.082222 | 0.108037 | 0.091920 | 0.068695 | 0.109447 | 0.117699 | 0.069518 | 0.092731 | 25.068673 | 23.411511 | 26.292878 | 27.794485 | 21.477597 | 21.123833 | 23.170715 | 24.047307 | 24.308825 | 20.495364 | 19.510025 | 21.608427 | 19.656883 | 25.246087 | 23.452166 | 21.890289 | 25.804780 | 25.128051 | 23.188366 | 23.196798 | 23.390063 | 22.127652 | 22.024997 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | 3.000000 | 7.000000 | 3.000000 | 7.000000 | 0.000000 | 3.000000 | 0.000000 | 0.000000 | 6.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.016667 | 15.000000 | 0.000000 | 0.000000 | 2.000000 | 1.000000 | 0.000000 | 16.000000 | 28.000000 | 31.000000 | 43.000000 | 0.000000 | 25.000000 | 0.000000 | 0.000000 | 4.000000 | 1.000000 | 1.000000 | 0.000000 | 10.000000 | 0.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | 12.000000 | 12.000000 | 0.000000 | 0.000000 | 0.000000 | 0.500000 | 0.137255 | 0.333333 | 0.375000 | 0.288518 | 0.309341 | 0.000000 | 0.387097 | 0.000000 | 0.000000 | 0.333333 | 0.000000 | 0.000000 | 0.370130 | 0.182482 | 26.888218 | 11.588261 | 21.255934 | 29.477773 | 2.546396 | 1.316357 | 2.740613 | 3.366422 | 4.984894 | 4.294346 | 8.610272 | 3.582218 | 2.028485 | 4.576857 | 7.466552 | 5.157531 | 4.661200 | 9.387139 | 2.481657 | 5.955978 | 6.689685 | 5.567544 | 4.661200 |
| 25% | 1.000000 | 5.000000 | 1.000000 | 0.000000 | 13.450000 | 182.000000 | 50.000000 | 32.000000 | 180.000000 | 37.000000 | 36.000000 | 294.000000 | 637.000000 | 469.000000 | 837.000000 | 13.000000 | 474.000000 | 72.000000 | 42.000000 | 477.000000 | 54.000000 | 54.00000 | 1.000000 | 6.000000 | 1.000000 | 0.000000 | 13.183333 | 167.000000 | 52.000000 | 40.000000 | 171.000000 | 33.000000 | 28.000000 | 279.000000 | 607.000000 | 446.000000 | 818.000000 | 17.000000 | 457.000000 | 73.000000 | 50.000000 | 466.000000 | 46.000000 | 38.000000 | 10.000000 | 286.000000 | 17.000000 | 8.000000 | 280.000000 | 14.000000 | 10.000000 | 316.000000 | 341.000000 | 0.300000 | 0.334988 | 0.660882 | 0.755922 | 0.338745 | 0.646387 | 0.625075 | 0.411054 | 0.490742 | 0.530485 | 0.595890 | 0.236082 | 0.141780 | 0.582101 | 0.242424 | 0.217391 | 0.511203 | 0.408163 | 62.580924 | 62.127751 | 51.143720 | 29.477773 | 67.112646 | 72.680190 | 69.378507 | 65.256798 | 70.069055 | 71.968062 | 72.701770 | 73.694432 | 75.744497 | 34.758204 | 64.458351 | 69.939577 | 60.897713 | 64.630988 | 69.335347 | 66.767372 | 56.214933 | 69.378507 | 70.263271 |
| 50% | 4.000000 | 12.000000 | 4.000000 | 1.000000 | 29.700000 | 378.000000 | 109.000000 | 78.000000 | 383.000000 | 64.000000 | 77.000000 | 585.000000 | 1236.000000 | 837.000000 | 1589.000000 | 32.000000 | 972.000000 | 153.000000 | 95.000000 | 994.000000 | 98.000000 | 115.00000 | 2.000000 | 11.000000 | 3.000000 | 1.000000 | 23.333333 | 320.000000 | 102.000000 | 86.000000 | 370.000000 | 64.000000 | 54.000000 | 533.000000 | 1226.000000 | 788.000000 | 1494.000000 | 37.000000 | 963.000000 | 143.000000 | 113.000000 | 998.000000 | 97.000000 | 74.000000 | 24.000000 | 611.000000 | 44.000000 | 21.000000 | 615.000000 | 29.000000 | 20.000000 | 694.000000 | 724.000000 | 0.409091 | 0.381393 | 0.720000 | 0.817424 | 0.390351 | 0.701584 | 0.689655 | 0.458634 | 0.551932 | 0.652174 | 0.645349 | 0.298997 | 0.190476 | 0.619556 | 0.311111 | 0.289474 | 0.561047 | 0.471344 | 89.404402 | 82.844195 | 79.974104 | 69.486405 | 85.412171 | 89.706517 | 87.721191 | 86.167458 | 87.656452 | 85.455330 | 88.195943 | 89.879154 | 89.339663 | 56.865285 | 85.304273 | 89.037549 | 85.779025 | 87.116962 | 88.713854 | 85.520069 | 77.233492 | 89.339663 | 89.339663 |
| 75% | 7.000000 | 25.000000 | 9.000000 | 3.000000 | 53.483333 | 559.000000 | 174.000000 | 140.000000 | 658.000000 | 112.000000 | 130.000000 | 888.000000 | 1963.000000 | 1298.000000 | 2442.000000 | 64.000000 | 1510.000000 | 250.000000 | 176.000000 | 1594.000000 | 160.000000 | 189.00000 | 5.000000 | 20.000000 | 6.000000 | 2.000000 | 41.316667 | 506.000000 | 166.000000 | 140.000000 | 604.000000 | 104.000000 | 91.000000 | 790.000000 | 1831.000000 | 1128.000000 | 2238.000000 | 59.000000 | 1428.000000 | 249.000000 | 176.000000 | 1597.000000 | 154.000000 | 135.000000 | 40.000000 | 940.000000 | 78.000000 | 38.000000 | 980.000000 | 48.000000 | 40.000000 | 1038.000000 | 1105.000000 | 0.503012 | 0.426824 | 0.783889 | 0.880588 | 0.443763 | 0.769231 | 0.751147 | 0.510962 | 0.611740 | 0.750000 | 0.698975 | 0.358855 | 0.246703 | 0.668511 | 0.366534 | 0.369231 | 0.603496 | 0.524494 | 95.899871 | 94.238239 | 93.202417 | 91.842900 | 95.338800 | 95.684074 | 95.036685 | 94.454035 | 95.317221 | 94.087182 | 95.403539 | 95.878291 | 95.878291 | 76.899827 | 94.928787 | 95.684074 | 95.295641 | 95.856711 | 95.662495 | 94.583513 | 92.015537 | 95.597756 | 95.770393 |
| max | 20.000000 | 90.000000 | 40.000000 | 11.000000 | 162.116667 | 2117.000000 | 723.000000 | 508.000000 | 2709.000000 | 359.000000 | 461.000000 | 3122.000000 | 6586.000000 | 3366.000000 | 6859.000000 | 274.000000 | 5254.000000 | 964.000000 | 630.000000 | 6034.000000 | 477.000000 | 656.00000 | 12.000000 | 74.000000 | 28.000000 | 10.000000 | 125.066667 | 1310.000000 | 454.000000 | 486.000000 | 1930.000000 | 293.000000 | 273.000000 | 2086.000000 | 5151.000000 | 2460.000000 | 5276.000000 | 165.000000 | 4005.000000 | 770.000000 | 611.000000 | 4894.000000 | 437.000000 | 420.000000 | 126.000000 | 2695.000000 | 316.000000 | 128.000000 | 2964.000000 | 144.000000 | 163.000000 | 3065.000000 | 3085.000000 | 1.000000 | 0.652361 | 1.000000 | 1.000000 | 0.651452 | 1.000000 | 1.000000 | 0.743243 | 0.830935 | 1.000000 | 0.812500 | 0.625000 | 0.666667 | 0.784615 | 0.687500 | 1.000000 | 0.730727 | 0.657095 | 100.000000 | 100.000000 | 99.956841 | 99.892102 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 99.956841 | 100.000000 | 100.000000 | 100.000000 | 98.531952 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 99.956841 | 100.000000 | 100.000000 | 100.000000 |
# List of columns to calculate means
columns_of_interest = [
"KD_PERCENTILE", "TD_PERCENTILE", "SUB.ATT_PERCENTILE", "REV_PERCENTILE", "CTRL_PERCENTILE",
"HEAD_PERCENTILE", "BODY_PERCENTILE", "LEG_PERCENTILE", "DISTANCE_PERCENTILE", "CLINCH_PERCENTILE",
"GROUND_PERCENTILE", "SIG_PERCENTILE", "STR_PERCENTILE", "STR_%_PERCENTILE", "TD_DEF_PERCENTILE",
"HEAD_DEF_PERCENTILE", "BODY_DEF_PERCENTILE", "LEG_DEF_PERCENTILE", "DISTANCE_DEF_PERCENTILE",
"CLINCH_DEF_PERCENTILE", "GROUND_DEF_PERCENTILE", "SIG_DEF_PERCENTILE", "STR_DEF_PERCENTILE"
]
# Calculate the mean for each of the specified columns
means2 = c_challengers[columns_of_interest].mean()
# Rank the means from highest to lowest and plot again
sorted_means2 = means2.sort_values(ascending=False)
# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_means2.plot(kind='bar', color='silver')
plt.title('Mean Percentiles across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90) # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()
# Display max rows
pd.set_option('display.max_rows', None)
print(sorted_means2)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)
STR_PERCENTILE 82.380380 GROUND_PERCENTILE 81.194476 HEAD_PERCENTILE 81.138135 SIG_PERCENTILE 80.868243 HEAD_DEF_PERCENTILE 79.846873 STR_DEF_PERCENTILE 79.824805 SIG_DEF_PERCENTILE 79.733994 CLINCH_PERCENTILE 79.283947 DISTANCE_DEF_PERCENTILE 78.909477 BODY_PERCENTILE 78.360612 CTRL_PERCENTILE 78.282105 DISTANCE_PERCENTILE 77.663619 CLINCH_DEF_PERCENTILE 76.950613 LEG_PERCENTILE 76.493828 LEG_DEF_PERCENTILE 76.238973 TD_DEF_PERCENTILE 76.084498 KD_PERCENTILE 75.908053 TD_PERCENTILE 75.432423 BODY_DEF_PERCENTILE 75.243381 GROUND_DEF_PERCENTILE 71.705494 SUB.ATT_PERCENTILE 71.692702 REV_PERCENTILE 62.963010 STR_%_PERCENTILE 55.675548 dtype: float64
# List of columns to calculate means
columns_of_interest = [
"TD_%", 'HEAD_%', 'BODY_%', 'LEG_%', 'DISTANCE_%', 'CLINCH_%', 'GROUND_%', 'SIG_%', 'STR_%', 'TD_DEF_%',
'HEAD_DEF_%', 'BODY_DEF_%', 'LEG_DEF_%', 'DISTANCE_DEF_%', 'CLINCH_DEF_%', 'GROUND_DEF_%', 'SIG_DEF_%', 'STR_DEF_%'
]
# Calculate the mean for each of the specified columns
meansB = c_challengers[columns_of_interest].mean()
# Rank the means from highest to lowest and plot again
sorted_meansB = meansB.sort_values(ascending=False)
# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_meansB.plot(kind='bar', color='turquoise')
plt.title('Mean Percentiles across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90) # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()
# Display max rows
pd.set_option('display.max_rows', None)
print(sorted_meansB)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)
LEG_% 0.817924 BODY_% 0.727326 CLINCH_% 0.706195 GROUND_% 0.688538 HEAD_DEF_% 0.639834 TD_DEF_% 0.625632 DISTANCE_DEF_% 0.618785 STR_% 0.556207 SIG_DEF_% 0.554091 STR_DEF_% 0.462211 SIG_% 0.461697 TD_% 0.416920 DISTANCE_% 0.389120 HEAD_% 0.380254 CLINCH_DEF_% 0.309191 GROUND_DEF_% 0.296140 BODY_DEF_% 0.293381 LEG_DEF_% 0.194047 dtype: float64
# Non Champions
c_non_champions = career[career['EITHER_CHAMP'] != 'Yes']
c_non_champions
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | KD | TD | SUB.ATT | REV | CTRL | HEAD | BODY | LEG | DISTANCE | CLINCH | GROUND | SIG | TOT.SIG | STR | TOTAL.STR | TOTAL.TD | TOTAL.HEAD | TOTAL.BODY | TOTAL.LEG | TOTAL.DISTANCE | TOTAL.CLINCH | TOTAL.GROUND | OPP_KD | OPP_TD | OPP_SUB.ATT | OPP_REV | OPP_CTRL | OPP_HEAD | OPP_BODY | OPP_LEG | OPP_DISTANCE | OPP_CLINCH | OPP_GROUND | OPP_SIG | OPP_TOT.SIG | OPP_STR | OPP_TOTAL.STR | OPP_TOTAL.TD | OPP_TOTAL.HEAD | OPP_TOTAL.BODY | OPP_TOTAL.LEG | OPP_TOTAL.DISTANCE | OPP_TOTAL.CLINCH | OPP_TOTAL.GROUND | TD_DEF | HEAD_DEF | BODY_DEF | LEG_DEF | DISTANCE_DEF | CLINCH_DEF | GROUND_DEF | SIG_DEF | STR_DEF | TD_% | HEAD_% | BODY_% | LEG_% | DISTANCE_% | CLINCH_% | GROUND_% | SIG_% | STR_% | TD_DEF_% | HEAD_DEF_% | BODY_DEF_% | LEG_DEF_% | DISTANCE_DEF_% | CLINCH_DEF_% | GROUND_DEF_% | SIG_DEF_% | STR_DEF_% | KD_PERCENTILE | TD_PERCENTILE | SUB.ATT_PERCENTILE | REV_PERCENTILE | CTRL_PERCENTILE | HEAD_PERCENTILE | BODY_PERCENTILE | LEG_PERCENTILE | DISTANCE_PERCENTILE | CLINCH_PERCENTILE | GROUND_PERCENTILE | SIG_PERCENTILE | STR_PERCENTILE | STR_%_PERCENTILE | TD_DEF_PERCENTILE | HEAD_DEF_PERCENTILE | BODY_DEF_PERCENTILE | LEG_DEF_PERCENTILE | DISTANCE_DEF_PERCENTILE | CLINCH_DEF_PERCENTILE | GROUND_DEF_PERCENTILE | SIG_DEF_PERCENTILE | STR_DEF_PERCENTILE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Asu Almabayev | None | No | No | No | No | 0.0 | 9.0 | 0.0 | 0.0 | 9.533333 | 22.0 | 9.0 | 13.0 | 23.0 | 8.0 | 13.0 | 44.0 | 77.0 | 85.0 | 132.0 | 14.0 | 53.0 | 11.0 | 13.0 | 51.0 | 10.0 | 16.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.233333 | 13.0 | 14.0 | 2.0 | 22.0 | 4.0 | 3.0 | 29.0 | 53.0 | 66.0 | 92.0 | 0.0 | 32.0 | 17.0 | 4.0 | 46.0 | 4.0 | 3.0 | 0.0 | 19.0 | 3.0 | 2.0 | 24.0 | 0.0 | 0.0 | 24.0 | 26.0 | 0.642857 | 0.415094 | 0.818182 | 1.000000 | 0.450980 | 0.800000 | 0.812500 | 0.571429 | 0.643939 | NaN | 0.593750 | 0.176471 | 0.500000 | 0.521739 | 0.000000 | 0.000000 | 0.452830 | 0.282609 | 26.888218 | 76.866638 | 21.255934 | 29.477773 | 57.833405 | 22.852827 | 26.866638 | 41.583945 | 21.709107 | 33.987915 | 49.482089 | 24.492879 | 27.492447 | 83.506045 | 7.466552 | 9.861890 | 23.327579 | 35.930082 | 15.235218 | 5.955978 | 6.689685 | 10.876133 | 10.142426 |
| 1 | CJ Vergara | Orthodox | No | No | No | No | 0.0 | 0.0 | 1.0 | 1.0 | 10.750000 | 203.0 | 149.0 | 52.0 | 285.0 | 35.0 | 84.0 | 404.0 | 700.0 | 579.0 | 895.0 | 0.0 | 448.0 | 193.0 | 59.0 | 531.0 | 46.0 | 123.0 | 2.0 | 16.0 | 5.0 | 2.0 | 20.200000 | 222.0 | 91.0 | 49.0 | 314.0 | 24.0 | 24.0 | 362.0 | 762.0 | 438.0 | 858.0 | 40.0 | 588.0 | 121.0 | 53.0 | 699.0 | 32.0 | 31.0 | 24.0 | 366.0 | 30.0 | 4.0 | 385.0 | 8.0 | 7.0 | 400.0 | 420.0 | NaN | 0.453125 | 0.772021 | 0.881356 | 0.536723 | 0.760870 | 0.682927 | 0.577143 | 0.646927 | 0.600000 | 0.622449 | 0.247934 | 0.075472 | 0.550787 | 0.250000 | 0.225806 | 0.524934 | 0.489510 | 26.888218 | 11.588261 | 51.143720 | 69.486405 | 60.595598 | 76.391886 | 93.029780 | 77.427708 | 82.088908 | 70.716444 | 89.512300 | 82.239965 | 81.635736 | 83.894646 | 85.304273 | 76.931377 | 76.413466 | 48.640483 | 78.161416 | 50.107898 | 45.986189 | 76.413466 | 76.132931 |
| 2 | Curtis Blaydes | Orthodox | No | No | No | No | 2.0 | 62.0 | 0.0 | 0.0 | 80.250000 | 395.0 | 69.0 | 110.0 | 260.0 | 73.0 | 241.0 | 574.0 | 1144.0 | 1016.0 | 1681.0 | 116.0 | 941.0 | 80.0 | 123.0 | 754.0 | 91.0 | 299.0 | 4.0 | 13.0 | 2.0 | 0.0 | 7.650000 | 224.0 | 47.0 | 27.0 | 242.0 | 41.0 | 15.0 | 298.0 | 739.0 | 530.0 | 985.0 | 19.0 | 640.0 | 67.0 | 32.0 | 668.0 | 52.0 | 19.0 | 6.0 | 416.0 | 20.0 | 5.0 | 426.0 | 11.0 | 4.0 | 441.0 | 455.0 | 0.534483 | 0.419766 | 0.862500 | 0.894309 | 0.344828 | 0.802198 | 0.806020 | 0.501748 | 0.604402 | 0.315789 | 0.650000 | 0.298507 | 0.156250 | 0.637725 | 0.211538 | 0.210526 | 0.596752 | 0.461929 | 76.542943 | 99.525248 | 21.255934 | 29.477773 | 98.575744 | 90.504963 | 77.924040 | 91.735002 | 79.909366 | 87.915408 | 99.050496 | 89.490721 | 92.706085 | 74.265976 | 50.841606 | 79.844627 | 64.911524 | 53.582218 | 80.146741 | 59.710833 | 33.448425 | 78.700906 | 78.485110 |
| 3 | Jailton Almeida | Orthodox | No | No | No | No | 0.0 | 25.0 | 8.0 | 1.0 | 47.950000 | 143.0 | 8.0 | 2.0 | 8.0 | 3.0 | 142.0 | 153.0 | 232.0 | 328.0 | 442.0 | 42.0 | 216.0 | 10.0 | 6.0 | 22.0 | 8.0 | 202.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.100000 | 34.0 | 3.0 | 1.0 | 4.0 | 1.0 | 33.0 | 38.0 | 68.0 | 74.0 | 115.0 | 2.0 | 61.0 | 6.0 | 1.0 | 19.0 | 1.0 | 48.0 | 2.0 | 27.0 | 3.0 | 0.0 | 15.0 | 0.0 | 15.0 | 30.0 | 41.0 | 0.595238 | 0.662037 | 0.800000 | 0.333333 | 0.363636 | 0.375000 | 0.702970 | 0.659483 | 0.742081 | 1.000000 | 0.442623 | 0.500000 | 0.000000 | 0.789474 | 0.000000 | 0.312500 | 0.441176 | 0.356522 | 26.888218 | 94.238239 | 91.562365 | 69.486405 | 94.216659 | 65.947346 | 24.622356 | 14.436772 | 11.631420 | 18.644799 | 96.266724 | 55.761761 | 64.954683 | 95.293610 | 28.226155 | 13.508848 | 23.327579 | 9.387139 | 11.242987 | 5.955978 | 68.558481 | 13.487268 | 15.537333 |
| 4 | Benoit Saint Denis | Southpaw | No | No | No | No | 4.0 | 16.0 | 5.0 | 1.0 | 24.416667 | 159.0 | 85.0 | 43.0 | 166.0 | 50.0 | 71.0 | 287.0 | 525.0 | 439.0 | 700.0 | 43.0 | 372.0 | 108.0 | 45.0 | 335.0 | 64.0 | 126.0 | 1.0 | 4.0 | 6.0 | 0.0 | 3.666667 | 159.0 | 67.0 | 25.0 | 193.0 | 50.0 | 8.0 | 251.0 | 439.0 | 299.0 | 497.0 | 13.0 | 336.0 | 76.0 | 27.0 | 360.0 | 71.0 | 8.0 | 9.0 | 177.0 | 9.0 | 2.0 | 167.0 | 21.0 | 0.0 | 188.0 | 198.0 | 0.372093 | 0.427419 | 0.787037 | 0.955556 | 0.495522 | 0.781250 | 0.563492 | 0.546667 | 0.627143 | 0.692308 | 0.526786 | 0.118421 | 0.074074 | 0.463889 | 0.295775 | 0.000000 | 0.428246 | 0.398390 | 89.404402 | 88.044886 | 83.707380 | 69.486405 | 81.614156 | 69.249029 | 82.585240 | 72.766508 | 67.695296 | 79.499353 | 86.944325 | 72.701770 | 73.176521 | 80.440415 | 61.609840 | 54.963315 | 44.993526 | 35.930082 | 54.294346 | 77.470868 | 6.689685 | 53.970652 | 53.172205 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2312 | Joao Roque | Orthodox | No | No | No | No | 0.0 | 1.0 | 0.0 | 0.0 | 0.266667 | 2.0 | 2.0 | 1.0 | 5.0 | 0.0 | 0.0 | 5.0 | 59.0 | 5.0 | 59.0 | 18.0 | 43.0 | 6.0 | 10.0 | 57.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 2.216667 | 4.0 | 0.0 | 0.0 | 3.0 | 1.0 | 0.0 | 4.0 | 84.0 | 18.0 | 98.0 | 0.0 | 84.0 | 0.0 | 0.0 | 77.0 | 1.0 | 6.0 | 0.0 | 80.0 | 0.0 | 0.0 | 74.0 | 0.0 | 6.0 | 80.0 | 80.0 | 0.055556 | 0.046512 | 0.333333 | 0.100000 | 0.087719 | 0.000000 | 0.000000 | 0.084746 | 0.084746 | NaN | 0.952381 | NaN | NaN | 0.961039 | 0.000000 | 1.000000 | 0.952381 | 0.816327 | 26.888218 | 29.736729 | 21.255934 | 29.477773 | 9.667674 | 5.049633 | 10.703496 | 9.451877 | 8.782909 | 4.294346 | 8.610272 | 5.114372 | 3.064307 | 0.604491 | 7.466552 | 33.275788 | 4.661200 | 9.387139 | 33.664221 | 5.955978 | 41.929219 | 30.621493 | 27.880880 |
| 2313 | Marcelo Aguiar | Orthodox | No | No | No | No | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 2.0 | 2.0 | 21.0 | 21.0 | 0.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 0.0 | 2.0 | 0.0 | 0.0 | 4.133333 | 14.0 | 4.0 | 0.0 | 0.0 | 0.0 | 18.0 | 18.0 | 31.0 | 29.0 | 42.0 | 3.0 | 27.0 | 4.0 | 0.0 | 2.0 | 0.0 | 29.0 | 1.0 | 13.0 | 0.0 | 0.0 | 2.0 | 0.0 | 11.0 | 13.0 | 13.0 | NaN | 1.000000 | NaN | NaN | NaN | NaN | 1.000000 | 1.000000 | 1.000000 | 0.333333 | 0.481481 | 0.000000 | NaN | 1.000000 | NaN | 0.379310 | 0.419355 | 0.309524 | 26.888218 | 11.588261 | 21.255934 | 29.477773 | 2.546396 | 5.049633 | 2.740613 | 3.366422 | 1.143720 | 4.294346 | 25.097108 | 2.675874 | 8.718170 | 99.827288 | 19.615883 | 7.034959 | 4.661200 | 9.387139 | 2.481657 | 5.955978 | 58.761329 | 6.258092 | 5.157531 |
| 2314 | Adrian Serrano | None | No | No | No | No | 0.0 | 0.0 | 0.0 | 1.0 | 3.116667 | 2.0 | 4.0 | 0.0 | 3.0 | 3.0 | 0.0 | 6.0 | 23.0 | 55.0 | 79.0 | 2.0 | 16.0 | 7.0 | 0.0 | 15.0 | 8.0 | 0.0 | 0.0 | 3.0 | 0.0 | 0.0 | 2.000000 | 15.0 | 5.0 | 6.0 | 21.0 | 3.0 | 2.0 | 26.0 | 55.0 | 51.0 | 85.0 | 6.0 | 41.0 | 7.0 | 7.0 | 44.0 | 5.0 | 6.0 | 3.0 | 26.0 | 2.0 | 1.0 | 23.0 | 2.0 | 4.0 | 29.0 | 34.0 | 0.000000 | 0.125000 | 0.571429 | NaN | 0.200000 | 0.375000 | NaN | 0.260870 | 0.696203 | 0.500000 | 0.634146 | 0.285714 | 0.142857 | 0.522727 | 0.400000 | 0.666667 | 0.527273 | 0.400000 | 26.888218 | 11.588261 | 21.255934 | 69.486405 | 31.160984 | 5.049633 | 15.645231 | 3.366422 | 6.560207 | 18.644799 | 8.610272 | 5.848079 | 19.443246 | 90.932642 | 35.261114 | 12.947777 | 18.277946 | 25.377644 | 14.889944 | 22.442814 | 33.448425 | 12.990937 | 13.163574 |
| 2315 | David Dodd | Orthodox | No | No | No | No | 0.0 | 0.0 | 0.0 | 0.0 | 0.033333 | 5.0 | 5.0 | 0.0 | 3.0 | 5.0 | 2.0 | 10.0 | 31.0 | 27.0 | 49.0 | 2.0 | 25.0 | 6.0 | 0.0 | 22.0 | 6.0 | 3.0 | 0.0 | 1.0 | 0.0 | 0.0 | 4.783333 | 11.0 | 3.0 | 2.0 | 12.0 | 4.0 | 0.0 | 16.0 | 60.0 | 105.0 | 163.0 | 1.0 | 53.0 | 5.0 | 2.0 | 45.0 | 13.0 | 2.0 | 0.0 | 42.0 | 2.0 | 0.0 | 33.0 | 9.0 | 2.0 | 44.0 | 58.0 | 0.000000 | 0.200000 | 0.833333 | NaN | 0.136364 | 0.833333 | 0.666667 | 0.322581 | 0.551020 | 0.000000 | 0.792453 | 0.400000 | 0.000000 | 0.733333 | 0.692308 | 1.000000 | 0.733333 | 0.355828 | 26.888218 | 11.588261 | 21.255934 | 29.477773 | 5.761761 | 8.610272 | 17.716875 | 3.366422 | 6.560207 | 25.312905 | 25.097108 | 8.394476 | 10.681916 | 56.563040 | 7.466552 | 19.594303 | 18.277946 | 9.387139 | 18.536901 | 53.323263 | 22.874407 | 18.536901 | 20.759603 |
| 2316 | Tyrone Roberts | Orthodox | No | No | No | No | 0.0 | 1.0 | 0.0 | 0.0 | 4.783333 | 11.0 | 3.0 | 2.0 | 12.0 | 4.0 | 0.0 | 16.0 | 60.0 | 105.0 | 163.0 | 1.0 | 53.0 | 5.0 | 2.0 | 45.0 | 13.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.033333 | 5.0 | 5.0 | 0.0 | 3.0 | 5.0 | 2.0 | 10.0 | 31.0 | 27.0 | 49.0 | 2.0 | 25.0 | 6.0 | 0.0 | 22.0 | 6.0 | 3.0 | 2.0 | 20.0 | 1.0 | 0.0 | 19.0 | 1.0 | 1.0 | 21.0 | 22.0 | 1.000000 | 0.207547 | 0.600000 | 1.000000 | 0.266667 | 0.307692 | 0.000000 | 0.266667 | 0.644172 | 1.000000 | 0.800000 | 0.166667 | NaN | 0.863636 | 0.166667 | 0.333333 | 0.677419 | 0.448980 | 26.888218 | 29.736729 | 21.255934 | 29.477773 | 39.879154 | 13.875701 | 13.465688 | 14.436772 | 14.954683 | 22.183858 | 8.610272 | 11.631420 | 32.801036 | 83.549223 | 28.226155 | 10.336642 | 12.473025 | 9.387139 | 12.947777 | 15.580492 | 16.508416 | 9.883470 | 8.696590 |
2212 rows × 100 columns
c_non_champions.describe()
| KD | TD | SUB.ATT | REV | CTRL | HEAD | BODY | LEG | DISTANCE | CLINCH | GROUND | SIG | TOT.SIG | STR | TOTAL.STR | TOTAL.TD | TOTAL.HEAD | TOTAL.BODY | TOTAL.LEG | TOTAL.DISTANCE | TOTAL.CLINCH | TOTAL.GROUND | OPP_KD | OPP_TD | OPP_SUB.ATT | OPP_REV | OPP_CTRL | OPP_HEAD | OPP_BODY | OPP_LEG | OPP_DISTANCE | OPP_CLINCH | OPP_GROUND | OPP_SIG | OPP_TOT.SIG | OPP_STR | OPP_TOTAL.STR | OPP_TOTAL.TD | OPP_TOTAL.HEAD | OPP_TOTAL.BODY | OPP_TOTAL.LEG | OPP_TOTAL.DISTANCE | OPP_TOTAL.CLINCH | OPP_TOTAL.GROUND | TD_DEF | HEAD_DEF | BODY_DEF | LEG_DEF | DISTANCE_DEF | CLINCH_DEF | GROUND_DEF | SIG_DEF | STR_DEF | TD_% | HEAD_% | BODY_% | LEG_% | DISTANCE_% | CLINCH_% | GROUND_% | SIG_% | STR_% | TD_DEF_% | HEAD_DEF_% | BODY_DEF_% | LEG_DEF_% | DISTANCE_DEF_% | CLINCH_DEF_% | GROUND_DEF_% | SIG_DEF_% | STR_DEF_% | KD_PERCENTILE | TD_PERCENTILE | SUB.ATT_PERCENTILE | REV_PERCENTILE | CTRL_PERCENTILE | HEAD_PERCENTILE | BODY_PERCENTILE | LEG_PERCENTILE | DISTANCE_PERCENTILE | CLINCH_PERCENTILE | GROUND_PERCENTILE | SIG_PERCENTILE | STR_PERCENTILE | STR_%_PERCENTILE | TD_DEF_PERCENTILE | HEAD_DEF_PERCENTILE | BODY_DEF_PERCENTILE | LEG_DEF_PERCENTILE | DISTANCE_DEF_PERCENTILE | CLINCH_DEF_PERCENTILE | GROUND_DEF_PERCENTILE | SIG_DEF_PERCENTILE | STR_DEF_PERCENTILE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 1981.000000 | 2201.000000 | 2124.000000 | 2086.000000 | 2203.000000 | 2071.000000 | 1872.000000 | 2208.000000 | 2211.000000 | 2025.000000 | 2206.000000 | 2142.000000 | 2083.000000 | 2202.000000 | 2080.000000 | 2057.000000 | 2209.000000 | 2210.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2211.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 | 2212.000000 |
| mean | 1.196655 | 6.070976 | 2.240054 | 0.813743 | 12.705048 | 128.974231 | 42.659584 | 33.820524 | 149.303797 | 28.845389 | 27.305154 | 205.454340 | 469.159584 | 307.912749 | 586.511302 | 16.406872 | 364.967902 | 62.124322 | 42.067360 | 387.593580 | 41.674503 | 39.891501 | 1.302893 | 6.388336 | 2.350814 | 0.819620 | 13.348825 | 134.397830 | 43.890145 | 34.611664 | 153.214738 | 29.881103 | 29.803797 | 212.899638 | 476.619801 | 318.518083 | 597.440325 | 16.552893 | 370.669078 | 63.174051 | 42.776673 | 390.350362 | 42.892857 | 43.376582 | 10.164557 | 236.271248 | 19.283906 | 8.165009 | 237.135624 | 13.011754 | 13.572785 | 263.720163 | 278.922242 | 0.363704 | 0.342294 | 0.684252 | 0.794655 | 0.370481 | 0.666827 | 0.678273 | 0.434385 | 0.529423 | 0.556787 | 0.605819 | 0.289229 | 0.188687 | 0.605248 | 0.303750 | 0.296509 | 0.523505 | 0.433996 | 48.526771 | 48.625918 | 48.813511 | 49.415623 | 48.492938 | 48.284673 | 48.418170 | 48.544565 | 48.459281 | 48.390279 | 48.322955 | 48.301677 | 48.258801 | 49.504118 | 48.620338 | 48.444774 | 48.658736 | 48.634337 | 48.468510 | 48.665624 | 49.057735 | 48.448657 | 48.453905 |
| std | 2.095358 | 9.083109 | 3.758273 | 1.415374 | 16.608481 | 157.106067 | 55.884705 | 47.880570 | 193.425908 | 39.013347 | 40.027201 | 245.613603 | 557.823609 | 349.754395 | 666.956357 | 23.118124 | 438.394571 | 81.522317 | 59.498245 | 483.687484 | 54.430332 | 58.307874 | 1.704155 | 7.266505 | 3.212775 | 1.421714 | 13.975472 | 146.895591 | 49.493931 | 42.736809 | 187.128538 | 35.236359 | 33.248219 | 228.742736 | 532.003947 | 311.648691 | 618.567347 | 18.857710 | 419.168494 | 72.377284 | 52.438897 | 472.447079 | 50.206573 | 49.199110 | 12.924967 | 278.462782 | 24.680846 | 11.072369 | 290.644969 | 16.278886 | 17.756294 | 309.597892 | 319.274652 | 0.251893 | 0.121814 | 0.173993 | 0.172481 | 0.118986 | 0.194211 | 0.205022 | 0.119757 | 0.132194 | 0.260587 | 0.119046 | 0.155346 | 0.155529 | 0.112038 | 0.169598 | 0.167221 | 0.104861 | 0.118656 | 25.711523 | 28.176033 | 27.218654 | 25.300067 | 28.390934 | 28.168098 | 28.239593 | 28.335174 | 28.254334 | 28.287326 | 28.166084 | 28.164904 | 28.157668 | 29.027402 | 28.320259 | 28.301331 | 28.329383 | 28.200302 | 28.302099 | 28.401996 | 28.663245 | 28.295610 | 28.295533 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 26.888218 | 11.588261 | 21.255934 | 29.477773 | 2.546396 | 1.316357 | 2.740613 | 3.366422 | 1.143720 | 4.294346 | 8.610272 | 0.604230 | 0.258956 | 0.237478 | 7.466552 | 0.215796 | 4.661200 | 9.387139 | 0.388433 | 5.955978 | 6.689685 | 0.151057 | 0.107898 |
| 25% | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 1.933333 | 23.000000 | 8.000000 | 5.000000 | 26.000000 | 4.750000 | 2.000000 | 43.000000 | 105.750000 | 71.000000 | 144.000000 | 2.000000 | 77.000000 | 12.000000 | 6.000000 | 76.000000 | 7.750000 | 3.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 3.550000 | 38.000000 | 10.000000 | 6.000000 | 31.000000 | 6.000000 | 7.000000 | 60.000000 | 126.000000 | 102.000000 | 177.000000 | 4.000000 | 95.000000 | 15.000000 | 8.000000 | 83.000000 | 9.000000 | 10.000000 | 1.000000 | 53.000000 | 3.000000 | 1.000000 | 49.000000 | 2.000000 | 2.000000 | 60.750000 | 67.000000 | 0.200000 | 0.280093 | 0.600000 | 0.728902 | 0.308261 | 0.583333 | 0.588235 | 0.372843 | 0.451211 | 0.400000 | 0.542107 | 0.200000 | 0.090909 | 0.545455 | 0.201846 | 0.200000 | 0.463768 | 0.357902 | 26.888218 | 29.736729 | 21.255934 | 29.477773 | 23.888649 | 23.672853 | 24.622356 | 24.665516 | 23.759171 | 24.530643 | 25.097108 | 24.018127 | 23.974968 | 24.287565 | 19.615883 | 23.716012 | 23.327579 | 25.377644 | 23.974968 | 22.442814 | 22.874407 | 24.023522 | 23.953388 |
| 50% | 0.000000 | 3.000000 | 1.000000 | 0.000000 | 6.800000 | 72.000000 | 23.000000 | 17.000000 | 78.000000 | 15.000000 | 12.000000 | 116.000000 | 261.500000 | 184.500000 | 351.500000 | 8.500000 | 205.000000 | 33.000000 | 22.000000 | 212.000000 | 22.000000 | 18.000000 | 1.000000 | 4.000000 | 1.000000 | 0.000000 | 9.341667 | 86.000000 | 27.000000 | 21.000000 | 88.000000 | 18.000000 | 19.000000 | 137.000000 | 292.000000 | 225.000000 | 392.000000 | 10.000000 | 222.500000 | 37.000000 | 26.000000 | 228.000000 | 26.000000 | 28.000000 | 5.000000 | 138.000000 | 10.000000 | 4.000000 | 134.000000 | 7.000000 | 8.000000 | 153.000000 | 166.000000 | 0.351351 | 0.343553 | 0.695652 | 0.819558 | 0.375000 | 0.685714 | 0.690778 | 0.436548 | 0.528110 | 0.588235 | 0.622117 | 0.286516 | 0.168831 | 0.605839 | 0.295268 | 0.292683 | 0.531034 | 0.444444 | 26.888218 | 49.352611 | 51.143720 | 29.477773 | 47.971515 | 47.863617 | 48.683643 | 48.079413 | 48.403107 | 48.252050 | 47.949935 | 47.906776 | 47.939145 | 49.222798 | 46.331463 | 48.252050 | 47.583082 | 48.640483 | 48.165732 | 46.719896 | 49.827363 | 48.230470 | 48.165732 |
| 75% | 2.000000 | 8.000000 | 3.000000 | 1.000000 | 16.604167 | 180.000000 | 56.000000 | 43.000000 | 197.500000 | 37.250000 | 36.000000 | 282.000000 | 627.000000 | 429.000000 | 802.250000 | 21.000000 | 491.250000 | 80.250000 | 53.000000 | 511.000000 | 54.000000 | 53.000000 | 2.000000 | 9.000000 | 3.000000 | 1.000000 | 18.137500 | 174.000000 | 60.000000 | 45.000000 | 205.000000 | 40.000000 | 40.000000 | 280.250000 | 633.000000 | 423.000000 | 799.500000 | 23.000000 | 491.250000 | 88.000000 | 57.000000 | 511.250000 | 57.000000 | 58.000000 | 14.000000 | 313.250000 | 26.000000 | 11.000000 | 317.000000 | 18.000000 | 18.000000 | 350.250000 | 372.000000 | 0.500000 | 0.411043 | 0.777778 | 0.900000 | 0.437031 | 0.785714 | 0.792327 | 0.500000 | 0.605620 | 0.735294 | 0.679163 | 0.369705 | 0.250000 | 0.665761 | 0.386547 | 0.380952 | 0.589333 | 0.513450 | 76.542943 | 74.082866 | 74.126025 | 69.486405 | 72.777298 | 72.399655 | 72.464394 | 72.766508 | 72.502158 | 72.156884 | 72.701770 | 72.313336 | 72.248597 | 74.589810 | 73.003884 | 72.566897 | 72.529132 | 72.701770 | 72.680190 | 73.543375 | 74.492879 | 72.550712 | 72.550712 |
| max | 20.000000 | 84.000000 | 48.000000 | 13.000000 | 156.500000 | 1146.000000 | 594.000000 | 425.000000 | 1478.000000 | 453.000000 | 334.000000 | 1896.000000 | 3783.000000 | 2525.000000 | 4550.000000 | 274.000000 | 2891.000000 | 817.000000 | 559.000000 | 3392.000000 | 576.000000 | 535.000000 | 11.000000 | 59.000000 | 29.000000 | 12.000000 | 128.183333 | 1069.000000 | 444.000000 | 330.000000 | 1558.000000 | 259.000000 | 273.000000 | 1825.000000 | 4464.000000 | 2273.000000 | 5118.000000 | 131.000000 | 3475.000000 | 672.000000 | 400.000000 | 3940.000000 | 417.000000 | 420.000000 | 102.000000 | 2426.000000 | 258.000000 | 82.000000 | 2557.000000 | 162.000000 | 163.000000 | 2743.000000 | 2845.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.868852 | 100.000000 | 99.956841 | 100.000000 | 100.000000 | 99.956841 | 99.827363 | 99.956841 | 99.956841 | 99.913681 | 100.000000 | 99.784204 | 99.956841 | 99.870522 | 99.827288 | 99.827363 | 99.913681 | 99.956841 | 99.697885 | 99.956841 | 100.000000 | 100.000000 | 99.956841 | 99.956841 |
# List of columns to calculate means
columns_of_interest = [
"KD_PERCENTILE", "TD_PERCENTILE", "SUB.ATT_PERCENTILE", "REV_PERCENTILE", "CTRL_PERCENTILE",
"HEAD_PERCENTILE", "BODY_PERCENTILE", "LEG_PERCENTILE", "DISTANCE_PERCENTILE", "CLINCH_PERCENTILE",
"GROUND_PERCENTILE", "SIG_PERCENTILE", "STR_PERCENTILE", "STR_%_PERCENTILE", "TD_DEF_PERCENTILE",
"HEAD_DEF_PERCENTILE", "BODY_DEF_PERCENTILE", "LEG_DEF_PERCENTILE", "DISTANCE_DEF_PERCENTILE",
"CLINCH_DEF_PERCENTILE", "GROUND_DEF_PERCENTILE", "SIG_DEF_PERCENTILE", "STR_DEF_PERCENTILE"
]
# Calculate the mean for each of the specified columns
means3 = c_non_champions[columns_of_interest].mean()
# Rank the means from highest to lowest and plot again
sorted_means3 = means3.sort_values(ascending=False)
# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_means3.plot(kind='bar', color='orangered')
plt.title('Mean Percentiles across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90) # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()
# Display max rows
pd.set_option('display.max_rows', None)
print(sorted_means3)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)
STR_%_PERCENTILE 49.504118 REV_PERCENTILE 49.415623 GROUND_DEF_PERCENTILE 49.057735 SUB.ATT_PERCENTILE 48.813511 CLINCH_DEF_PERCENTILE 48.665624 BODY_DEF_PERCENTILE 48.658736 LEG_DEF_PERCENTILE 48.634337 TD_PERCENTILE 48.625918 TD_DEF_PERCENTILE 48.620338 LEG_PERCENTILE 48.544565 KD_PERCENTILE 48.526771 CTRL_PERCENTILE 48.492938 DISTANCE_DEF_PERCENTILE 48.468510 DISTANCE_PERCENTILE 48.459281 STR_DEF_PERCENTILE 48.453905 SIG_DEF_PERCENTILE 48.448657 HEAD_DEF_PERCENTILE 48.444774 BODY_PERCENTILE 48.418170 CLINCH_PERCENTILE 48.390279 GROUND_PERCENTILE 48.322955 SIG_PERCENTILE 48.301677 HEAD_PERCENTILE 48.284673 STR_PERCENTILE 48.258801 dtype: float64
# List of columns to calculate means
columns_of_interest = [
"TD_%", 'HEAD_%', 'BODY_%', 'LEG_%', 'DISTANCE_%', 'CLINCH_%', 'GROUND_%', 'SIG_%', 'STR_%', 'TD_DEF_%',
'HEAD_DEF_%', 'BODY_DEF_%', 'LEG_DEF_%', 'DISTANCE_DEF_%', 'CLINCH_DEF_%', 'GROUND_DEF_%', 'SIG_DEF_%', 'STR_DEF_%'
]
# Calculate the mean for each of the specified columns
meansC = c_non_champions[columns_of_interest].mean()
# Rank the means from highest to lowest and plot again
sorted_meansC = meansC.sort_values(ascending=False)
# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_meansC.plot(kind='bar', color='palevioletred')
plt.title('Mean Percentiles across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90) # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()
# Display max rows
pd.set_option('display.max_rows', None)
print(sorted_meansC)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)
LEG_% 0.794655 BODY_% 0.684252 GROUND_% 0.678273 CLINCH_% 0.666827 HEAD_DEF_% 0.605819 DISTANCE_DEF_% 0.605248 TD_DEF_% 0.556787 STR_% 0.529423 SIG_DEF_% 0.523505 SIG_% 0.434385 STR_DEF_% 0.433996 DISTANCE_% 0.370481 TD_% 0.363704 HEAD_% 0.342294 CLINCH_DEF_% 0.303750 GROUND_DEF_% 0.296509 BODY_DEF_% 0.289229 LEG_DEF_% 0.188687 dtype: float64
# Non Challengers
c_non_challengers = career[career['EVER_CHALLENGER'] != 'Yes']
c_non_challengers
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | KD | TD | SUB.ATT | REV | CTRL | HEAD | BODY | LEG | DISTANCE | CLINCH | GROUND | SIG | TOT.SIG | STR | TOTAL.STR | TOTAL.TD | TOTAL.HEAD | TOTAL.BODY | TOTAL.LEG | TOTAL.DISTANCE | TOTAL.CLINCH | TOTAL.GROUND | OPP_KD | OPP_TD | OPP_SUB.ATT | OPP_REV | OPP_CTRL | OPP_HEAD | OPP_BODY | OPP_LEG | OPP_DISTANCE | OPP_CLINCH | OPP_GROUND | OPP_SIG | OPP_TOT.SIG | OPP_STR | OPP_TOTAL.STR | OPP_TOTAL.TD | OPP_TOTAL.HEAD | OPP_TOTAL.BODY | OPP_TOTAL.LEG | OPP_TOTAL.DISTANCE | OPP_TOTAL.CLINCH | OPP_TOTAL.GROUND | TD_DEF | HEAD_DEF | BODY_DEF | LEG_DEF | DISTANCE_DEF | CLINCH_DEF | GROUND_DEF | SIG_DEF | STR_DEF | TD_% | HEAD_% | BODY_% | LEG_% | DISTANCE_% | CLINCH_% | GROUND_% | SIG_% | STR_% | TD_DEF_% | HEAD_DEF_% | BODY_DEF_% | LEG_DEF_% | DISTANCE_DEF_% | CLINCH_DEF_% | GROUND_DEF_% | SIG_DEF_% | STR_DEF_% | KD_PERCENTILE | TD_PERCENTILE | SUB.ATT_PERCENTILE | REV_PERCENTILE | CTRL_PERCENTILE | HEAD_PERCENTILE | BODY_PERCENTILE | LEG_PERCENTILE | DISTANCE_PERCENTILE | CLINCH_PERCENTILE | GROUND_PERCENTILE | SIG_PERCENTILE | STR_PERCENTILE | STR_%_PERCENTILE | TD_DEF_PERCENTILE | HEAD_DEF_PERCENTILE | BODY_DEF_PERCENTILE | LEG_DEF_PERCENTILE | DISTANCE_DEF_PERCENTILE | CLINCH_DEF_PERCENTILE | GROUND_DEF_PERCENTILE | SIG_DEF_PERCENTILE | STR_DEF_PERCENTILE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Asu Almabayev | None | No | No | No | No | 0.0 | 9.0 | 0.0 | 0.0 | 9.533333 | 22.0 | 9.0 | 13.0 | 23.0 | 8.0 | 13.0 | 44.0 | 77.0 | 85.0 | 132.0 | 14.0 | 53.0 | 11.0 | 13.0 | 51.0 | 10.0 | 16.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.233333 | 13.0 | 14.0 | 2.0 | 22.0 | 4.0 | 3.0 | 29.0 | 53.0 | 66.0 | 92.0 | 0.0 | 32.0 | 17.0 | 4.0 | 46.0 | 4.0 | 3.0 | 0.0 | 19.0 | 3.0 | 2.0 | 24.0 | 0.0 | 0.0 | 24.0 | 26.0 | 0.642857 | 0.415094 | 0.818182 | 1.000000 | 0.450980 | 0.800000 | 0.812500 | 0.571429 | 0.643939 | NaN | 0.593750 | 0.176471 | 0.500000 | 0.521739 | 0.000000 | 0.000000 | 0.452830 | 0.282609 | 26.888218 | 76.866638 | 21.255934 | 29.477773 | 57.833405 | 22.852827 | 26.866638 | 41.583945 | 21.709107 | 33.987915 | 49.482089 | 24.492879 | 27.492447 | 83.506045 | 7.466552 | 9.861890 | 23.327579 | 35.930082 | 15.235218 | 5.955978 | 6.689685 | 10.876133 | 10.142426 |
| 1 | CJ Vergara | Orthodox | No | No | No | No | 0.0 | 0.0 | 1.0 | 1.0 | 10.750000 | 203.0 | 149.0 | 52.0 | 285.0 | 35.0 | 84.0 | 404.0 | 700.0 | 579.0 | 895.0 | 0.0 | 448.0 | 193.0 | 59.0 | 531.0 | 46.0 | 123.0 | 2.0 | 16.0 | 5.0 | 2.0 | 20.200000 | 222.0 | 91.0 | 49.0 | 314.0 | 24.0 | 24.0 | 362.0 | 762.0 | 438.0 | 858.0 | 40.0 | 588.0 | 121.0 | 53.0 | 699.0 | 32.0 | 31.0 | 24.0 | 366.0 | 30.0 | 4.0 | 385.0 | 8.0 | 7.0 | 400.0 | 420.0 | NaN | 0.453125 | 0.772021 | 0.881356 | 0.536723 | 0.760870 | 0.682927 | 0.577143 | 0.646927 | 0.600000 | 0.622449 | 0.247934 | 0.075472 | 0.550787 | 0.250000 | 0.225806 | 0.524934 | 0.489510 | 26.888218 | 11.588261 | 51.143720 | 69.486405 | 60.595598 | 76.391886 | 93.029780 | 77.427708 | 82.088908 | 70.716444 | 89.512300 | 82.239965 | 81.635736 | 83.894646 | 85.304273 | 76.931377 | 76.413466 | 48.640483 | 78.161416 | 50.107898 | 45.986189 | 76.413466 | 76.132931 |
| 2 | Curtis Blaydes | Orthodox | No | No | No | No | 2.0 | 62.0 | 0.0 | 0.0 | 80.250000 | 395.0 | 69.0 | 110.0 | 260.0 | 73.0 | 241.0 | 574.0 | 1144.0 | 1016.0 | 1681.0 | 116.0 | 941.0 | 80.0 | 123.0 | 754.0 | 91.0 | 299.0 | 4.0 | 13.0 | 2.0 | 0.0 | 7.650000 | 224.0 | 47.0 | 27.0 | 242.0 | 41.0 | 15.0 | 298.0 | 739.0 | 530.0 | 985.0 | 19.0 | 640.0 | 67.0 | 32.0 | 668.0 | 52.0 | 19.0 | 6.0 | 416.0 | 20.0 | 5.0 | 426.0 | 11.0 | 4.0 | 441.0 | 455.0 | 0.534483 | 0.419766 | 0.862500 | 0.894309 | 0.344828 | 0.802198 | 0.806020 | 0.501748 | 0.604402 | 0.315789 | 0.650000 | 0.298507 | 0.156250 | 0.637725 | 0.211538 | 0.210526 | 0.596752 | 0.461929 | 76.542943 | 99.525248 | 21.255934 | 29.477773 | 98.575744 | 90.504963 | 77.924040 | 91.735002 | 79.909366 | 87.915408 | 99.050496 | 89.490721 | 92.706085 | 74.265976 | 50.841606 | 79.844627 | 64.911524 | 53.582218 | 80.146741 | 59.710833 | 33.448425 | 78.700906 | 78.485110 |
| 3 | Jailton Almeida | Orthodox | No | No | No | No | 0.0 | 25.0 | 8.0 | 1.0 | 47.950000 | 143.0 | 8.0 | 2.0 | 8.0 | 3.0 | 142.0 | 153.0 | 232.0 | 328.0 | 442.0 | 42.0 | 216.0 | 10.0 | 6.0 | 22.0 | 8.0 | 202.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.100000 | 34.0 | 3.0 | 1.0 | 4.0 | 1.0 | 33.0 | 38.0 | 68.0 | 74.0 | 115.0 | 2.0 | 61.0 | 6.0 | 1.0 | 19.0 | 1.0 | 48.0 | 2.0 | 27.0 | 3.0 | 0.0 | 15.0 | 0.0 | 15.0 | 30.0 | 41.0 | 0.595238 | 0.662037 | 0.800000 | 0.333333 | 0.363636 | 0.375000 | 0.702970 | 0.659483 | 0.742081 | 1.000000 | 0.442623 | 0.500000 | 0.000000 | 0.789474 | 0.000000 | 0.312500 | 0.441176 | 0.356522 | 26.888218 | 94.238239 | 91.562365 | 69.486405 | 94.216659 | 65.947346 | 24.622356 | 14.436772 | 11.631420 | 18.644799 | 96.266724 | 55.761761 | 64.954683 | 95.293610 | 28.226155 | 13.508848 | 23.327579 | 9.387139 | 11.242987 | 5.955978 | 68.558481 | 13.487268 | 15.537333 |
| 4 | Benoit Saint Denis | Southpaw | No | No | No | No | 4.0 | 16.0 | 5.0 | 1.0 | 24.416667 | 159.0 | 85.0 | 43.0 | 166.0 | 50.0 | 71.0 | 287.0 | 525.0 | 439.0 | 700.0 | 43.0 | 372.0 | 108.0 | 45.0 | 335.0 | 64.0 | 126.0 | 1.0 | 4.0 | 6.0 | 0.0 | 3.666667 | 159.0 | 67.0 | 25.0 | 193.0 | 50.0 | 8.0 | 251.0 | 439.0 | 299.0 | 497.0 | 13.0 | 336.0 | 76.0 | 27.0 | 360.0 | 71.0 | 8.0 | 9.0 | 177.0 | 9.0 | 2.0 | 167.0 | 21.0 | 0.0 | 188.0 | 198.0 | 0.372093 | 0.427419 | 0.787037 | 0.955556 | 0.495522 | 0.781250 | 0.563492 | 0.546667 | 0.627143 | 0.692308 | 0.526786 | 0.118421 | 0.074074 | 0.463889 | 0.295775 | 0.000000 | 0.428246 | 0.398390 | 89.404402 | 88.044886 | 83.707380 | 69.486405 | 81.614156 | 69.249029 | 82.585240 | 72.766508 | 67.695296 | 79.499353 | 86.944325 | 72.701770 | 73.176521 | 80.440415 | 61.609840 | 54.963315 | 44.993526 | 35.930082 | 54.294346 | 77.470868 | 6.689685 | 53.970652 | 53.172205 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2312 | Joao Roque | Orthodox | No | No | No | No | 0.0 | 1.0 | 0.0 | 0.0 | 0.266667 | 2.0 | 2.0 | 1.0 | 5.0 | 0.0 | 0.0 | 5.0 | 59.0 | 5.0 | 59.0 | 18.0 | 43.0 | 6.0 | 10.0 | 57.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 2.216667 | 4.0 | 0.0 | 0.0 | 3.0 | 1.0 | 0.0 | 4.0 | 84.0 | 18.0 | 98.0 | 0.0 | 84.0 | 0.0 | 0.0 | 77.0 | 1.0 | 6.0 | 0.0 | 80.0 | 0.0 | 0.0 | 74.0 | 0.0 | 6.0 | 80.0 | 80.0 | 0.055556 | 0.046512 | 0.333333 | 0.100000 | 0.087719 | 0.000000 | 0.000000 | 0.084746 | 0.084746 | NaN | 0.952381 | NaN | NaN | 0.961039 | 0.000000 | 1.000000 | 0.952381 | 0.816327 | 26.888218 | 29.736729 | 21.255934 | 29.477773 | 9.667674 | 5.049633 | 10.703496 | 9.451877 | 8.782909 | 4.294346 | 8.610272 | 5.114372 | 3.064307 | 0.604491 | 7.466552 | 33.275788 | 4.661200 | 9.387139 | 33.664221 | 5.955978 | 41.929219 | 30.621493 | 27.880880 |
| 2313 | Marcelo Aguiar | Orthodox | No | No | No | No | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 2.0 | 2.0 | 21.0 | 21.0 | 0.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 0.0 | 2.0 | 0.0 | 0.0 | 4.133333 | 14.0 | 4.0 | 0.0 | 0.0 | 0.0 | 18.0 | 18.0 | 31.0 | 29.0 | 42.0 | 3.0 | 27.0 | 4.0 | 0.0 | 2.0 | 0.0 | 29.0 | 1.0 | 13.0 | 0.0 | 0.0 | 2.0 | 0.0 | 11.0 | 13.0 | 13.0 | NaN | 1.000000 | NaN | NaN | NaN | NaN | 1.000000 | 1.000000 | 1.000000 | 0.333333 | 0.481481 | 0.000000 | NaN | 1.000000 | NaN | 0.379310 | 0.419355 | 0.309524 | 26.888218 | 11.588261 | 21.255934 | 29.477773 | 2.546396 | 5.049633 | 2.740613 | 3.366422 | 1.143720 | 4.294346 | 25.097108 | 2.675874 | 8.718170 | 99.827288 | 19.615883 | 7.034959 | 4.661200 | 9.387139 | 2.481657 | 5.955978 | 58.761329 | 6.258092 | 5.157531 |
| 2314 | Adrian Serrano | None | No | No | No | No | 0.0 | 0.0 | 0.0 | 1.0 | 3.116667 | 2.0 | 4.0 | 0.0 | 3.0 | 3.0 | 0.0 | 6.0 | 23.0 | 55.0 | 79.0 | 2.0 | 16.0 | 7.0 | 0.0 | 15.0 | 8.0 | 0.0 | 0.0 | 3.0 | 0.0 | 0.0 | 2.000000 | 15.0 | 5.0 | 6.0 | 21.0 | 3.0 | 2.0 | 26.0 | 55.0 | 51.0 | 85.0 | 6.0 | 41.0 | 7.0 | 7.0 | 44.0 | 5.0 | 6.0 | 3.0 | 26.0 | 2.0 | 1.0 | 23.0 | 2.0 | 4.0 | 29.0 | 34.0 | 0.000000 | 0.125000 | 0.571429 | NaN | 0.200000 | 0.375000 | NaN | 0.260870 | 0.696203 | 0.500000 | 0.634146 | 0.285714 | 0.142857 | 0.522727 | 0.400000 | 0.666667 | 0.527273 | 0.400000 | 26.888218 | 11.588261 | 21.255934 | 69.486405 | 31.160984 | 5.049633 | 15.645231 | 3.366422 | 6.560207 | 18.644799 | 8.610272 | 5.848079 | 19.443246 | 90.932642 | 35.261114 | 12.947777 | 18.277946 | 25.377644 | 14.889944 | 22.442814 | 33.448425 | 12.990937 | 13.163574 |
| 2315 | David Dodd | Orthodox | No | No | No | No | 0.0 | 0.0 | 0.0 | 0.0 | 0.033333 | 5.0 | 5.0 | 0.0 | 3.0 | 5.0 | 2.0 | 10.0 | 31.0 | 27.0 | 49.0 | 2.0 | 25.0 | 6.0 | 0.0 | 22.0 | 6.0 | 3.0 | 0.0 | 1.0 | 0.0 | 0.0 | 4.783333 | 11.0 | 3.0 | 2.0 | 12.0 | 4.0 | 0.0 | 16.0 | 60.0 | 105.0 | 163.0 | 1.0 | 53.0 | 5.0 | 2.0 | 45.0 | 13.0 | 2.0 | 0.0 | 42.0 | 2.0 | 0.0 | 33.0 | 9.0 | 2.0 | 44.0 | 58.0 | 0.000000 | 0.200000 | 0.833333 | NaN | 0.136364 | 0.833333 | 0.666667 | 0.322581 | 0.551020 | 0.000000 | 0.792453 | 0.400000 | 0.000000 | 0.733333 | 0.692308 | 1.000000 | 0.733333 | 0.355828 | 26.888218 | 11.588261 | 21.255934 | 29.477773 | 5.761761 | 8.610272 | 17.716875 | 3.366422 | 6.560207 | 25.312905 | 25.097108 | 8.394476 | 10.681916 | 56.563040 | 7.466552 | 19.594303 | 18.277946 | 9.387139 | 18.536901 | 53.323263 | 22.874407 | 18.536901 | 20.759603 |
| 2316 | Tyrone Roberts | Orthodox | No | No | No | No | 0.0 | 1.0 | 0.0 | 0.0 | 4.783333 | 11.0 | 3.0 | 2.0 | 12.0 | 4.0 | 0.0 | 16.0 | 60.0 | 105.0 | 163.0 | 1.0 | 53.0 | 5.0 | 2.0 | 45.0 | 13.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.033333 | 5.0 | 5.0 | 0.0 | 3.0 | 5.0 | 2.0 | 10.0 | 31.0 | 27.0 | 49.0 | 2.0 | 25.0 | 6.0 | 0.0 | 22.0 | 6.0 | 3.0 | 2.0 | 20.0 | 1.0 | 0.0 | 19.0 | 1.0 | 1.0 | 21.0 | 22.0 | 1.000000 | 0.207547 | 0.600000 | 1.000000 | 0.266667 | 0.307692 | 0.000000 | 0.266667 | 0.644172 | 1.000000 | 0.800000 | 0.166667 | NaN | 0.863636 | 0.166667 | 0.333333 | 0.677419 | 0.448980 | 26.888218 | 29.736729 | 21.255934 | 29.477773 | 39.879154 | 13.875701 | 13.465688 | 14.436772 | 14.954683 | 22.183858 | 8.610272 | 11.631420 | 32.801036 | 83.549223 | 28.226155 | 10.336642 | 12.473025 | 9.387139 | 12.947777 | 15.580492 | 16.508416 | 9.883470 | 8.696590 |
2096 rows × 100 columns
c_non_challengers.describe()
| KD | TD | SUB.ATT | REV | CTRL | HEAD | BODY | LEG | DISTANCE | CLINCH | GROUND | SIG | TOT.SIG | STR | TOTAL.STR | TOTAL.TD | TOTAL.HEAD | TOTAL.BODY | TOTAL.LEG | TOTAL.DISTANCE | TOTAL.CLINCH | TOTAL.GROUND | OPP_KD | OPP_TD | OPP_SUB.ATT | OPP_REV | OPP_CTRL | OPP_HEAD | OPP_BODY | OPP_LEG | OPP_DISTANCE | OPP_CLINCH | OPP_GROUND | OPP_SIG | OPP_TOT.SIG | OPP_STR | OPP_TOTAL.STR | OPP_TOTAL.TD | OPP_TOTAL.HEAD | OPP_TOTAL.BODY | OPP_TOTAL.LEG | OPP_TOTAL.DISTANCE | OPP_TOTAL.CLINCH | OPP_TOTAL.GROUND | TD_DEF | HEAD_DEF | BODY_DEF | LEG_DEF | DISTANCE_DEF | CLINCH_DEF | GROUND_DEF | SIG_DEF | STR_DEF | TD_% | HEAD_% | BODY_% | LEG_% | DISTANCE_% | CLINCH_% | GROUND_% | SIG_% | STR_% | TD_DEF_% | HEAD_DEF_% | BODY_DEF_% | LEG_DEF_% | DISTANCE_DEF_% | CLINCH_DEF_% | GROUND_DEF_% | SIG_DEF_% | STR_DEF_% | KD_PERCENTILE | TD_PERCENTILE | SUB.ATT_PERCENTILE | REV_PERCENTILE | CTRL_PERCENTILE | HEAD_PERCENTILE | BODY_PERCENTILE | LEG_PERCENTILE | DISTANCE_PERCENTILE | CLINCH_PERCENTILE | GROUND_PERCENTILE | SIG_PERCENTILE | STR_PERCENTILE | STR_%_PERCENTILE | TD_DEF_PERCENTILE | HEAD_DEF_PERCENTILE | BODY_DEF_PERCENTILE | LEG_DEF_PERCENTILE | DISTANCE_DEF_PERCENTILE | CLINCH_DEF_PERCENTILE | GROUND_DEF_PERCENTILE | SIG_DEF_PERCENTILE | STR_DEF_PERCENTILE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.00000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 1866.000000 | 2085.000000 | 2009.000000 | 1971.000000 | 2087.000000 | 1956.000000 | 1758.000000 | 2092.000000 | 2095.000000 | 1911.000000 | 2090.000000 | 2027.000000 | 1969.000000 | 2086.000000 | 1964.000000 | 1941.000000 | 2093.000000 | 2094.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2095.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 | 2096.000000 |
| mean | 1.058683 | 5.600191 | 2.057729 | 0.769084 | 11.627886 | 118.088263 | 39.458492 | 31.307252 | 137.583492 | 26.800095 | 24.47042 | 188.854008 | 430.268130 | 282.533397 | 537.821565 | 15.099237 | 333.883111 | 57.521947 | 38.863073 | 355.875000 | 38.735210 | 35.657920 | 1.218034 | 5.976145 | 2.235210 | 0.785782 | 12.490689 | 123.501908 | 40.728531 | 31.361164 | 140.180821 | 27.770515 | 27.640267 | 195.591603 | 436.952767 | 293.382156 | 549.156011 | 15.417939 | 339.630725 | 58.531489 | 38.790553 | 356.888836 | 39.771469 | 40.292462 | 9.441794 | 216.128817 | 17.802958 | 7.429389 | 216.708015 | 12.000954 | 12.652195 | 241.361164 | 255.773855 | 0.363409 | 0.341382 | 0.683110 | 0.794597 | 0.370691 | 0.664954 | 0.677831 | 0.434040 | 0.528742 | 0.554721 | 0.604784 | 0.289315 | 0.188770 | 0.604814 | 0.303448 | 0.297266 | 0.522517 | 0.433401 | 47.292138 | 47.342288 | 47.736600 | 48.657049 | 47.041820 | 46.740683 | 47.033542 | 47.230374 | 47.107032 | 46.936187 | 46.734743 | 46.769140 | 46.609702 | 49.425157 | 47.273533 | 46.876833 | 47.362220 | 47.257246 | 46.975671 | 47.182211 | 47.735251 | 46.888734 | 46.879159 |
| std | 1.854850 | 8.458207 | 3.500836 | 1.367931 | 15.023103 | 144.432066 | 52.294779 | 44.722481 | 178.861586 | 37.052445 | 35.74229 | 226.900015 | 511.715937 | 321.367907 | 611.521210 | 21.076804 | 400.202746 | 76.471896 | 55.432479 | 443.579615 | 51.602511 | 51.693459 | 1.599725 | 6.785173 | 3.056121 | 1.373256 | 13.050976 | 132.989145 | 45.593841 | 37.683759 | 168.602882 | 32.934249 | 30.651593 | 206.207487 | 482.027059 | 282.332654 | 561.447237 | 17.688175 | 380.467499 | 66.414012 | 46.291971 | 428.350204 | 46.757453 | 45.348051 | 12.143904 | 253.569742 | 22.560506 | 10.019445 | 264.682975 | 15.132478 | 16.353165 | 281.809804 | 290.978365 | 0.256233 | 0.123483 | 0.176488 | 0.175808 | 0.120782 | 0.197880 | 0.209698 | 0.121770 | 0.133631 | 0.264101 | 0.120664 | 0.157418 | 0.158293 | 0.113824 | 0.172052 | 0.170125 | 0.106305 | 0.119718 | 25.063888 | 27.812455 | 26.781905 | 24.960319 | 27.927503 | 27.601150 | 27.771251 | 27.901275 | 27.756811 | 27.869332 | 27.627323 | 27.597697 | 27.549108 | 29.171644 | 27.922652 | 27.705184 | 27.856337 | 27.674830 | 27.710033 | 27.888316 | 28.391933 | 27.699548 | 27.696789 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 26.888218 | 11.588261 | 21.255934 | 29.477773 | 2.546396 | 1.316357 | 2.740613 | 3.366422 | 1.143720 | 4.294346 | 8.610272 | 0.604230 | 0.258956 | 0.237478 | 7.466552 | 0.215796 | 4.661200 | 9.387139 | 0.388433 | 5.955978 | 6.689685 | 0.151057 | 0.107898 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.833333 | 22.000000 | 7.000000 | 4.000000 | 25.000000 | 4.000000 | 2.00000 | 40.000000 | 98.000000 | 67.000000 | 136.000000 | 2.000000 | 73.000000 | 11.000000 | 6.000000 | 72.000000 | 7.000000 | 2.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 3.333333 | 36.000000 | 9.750000 | 6.000000 | 29.000000 | 6.000000 | 6.000000 | 57.000000 | 119.750000 | 97.750000 | 168.750000 | 3.000000 | 91.000000 | 14.000000 | 7.000000 | 78.000000 | 8.000000 | 9.000000 | 1.000000 | 51.000000 | 3.000000 | 1.000000 | 46.000000 | 2.000000 | 2.000000 | 57.750000 | 64.000000 | 0.191244 | 0.278351 | 0.600000 | 0.727273 | 0.307692 | 0.576923 | 0.583333 | 0.371647 | 0.450787 | 0.394886 | 0.539619 | 0.200000 | 0.086957 | 0.544782 | 0.200000 | 0.200000 | 0.462069 | 0.355932 | 26.888218 | 11.588261 | 21.255934 | 29.477773 | 22.895986 | 22.852827 | 22.183858 | 21.514890 | 23.003884 | 22.183858 | 25.097108 | 22.809668 | 22.831247 | 24.114853 | 19.615883 | 22.982305 | 23.327579 | 25.377644 | 23.154942 | 22.442814 | 22.874407 | 22.966120 | 22.744929 |
| 50% | 0.000000 | 3.000000 | 1.000000 | 0.000000 | 6.383333 | 67.000000 | 21.000000 | 16.000000 | 73.000000 | 14.000000 | 11.00000 | 107.000000 | 246.500000 | 169.500000 | 330.500000 | 8.000000 | 192.000000 | 31.000000 | 20.000000 | 199.000000 | 21.000000 | 16.000000 | 1.000000 | 4.000000 | 1.000000 | 0.000000 | 8.900000 | 81.000000 | 25.000000 | 19.000000 | 81.500000 | 17.000000 | 18.000000 | 129.000000 | 274.000000 | 211.000000 | 366.000000 | 10.000000 | 208.000000 | 35.000000 | 24.000000 | 214.500000 | 24.000000 | 26.000000 | 5.000000 | 129.500000 | 10.000000 | 4.000000 | 126.000000 | 7.000000 | 7.000000 | 145.000000 | 157.000000 | 0.349415 | 0.342282 | 0.694656 | 0.822222 | 0.375000 | 0.686511 | 0.692308 | 0.436548 | 0.528724 | 0.588235 | 0.620780 | 0.286957 | 0.166667 | 0.605207 | 0.294118 | 0.294118 | 0.529459 | 0.443433 | 26.888218 | 49.352611 | 51.143720 | 29.477773 | 46.180406 | 46.137246 | 46.266724 | 46.568839 | 46.568839 | 46.719896 | 46.417782 | 46.094087 | 45.932240 | 49.352332 | 46.331463 | 46.331463 | 47.583082 | 48.640483 | 46.568839 | 46.719896 | 45.986189 | 46.353043 | 46.374622 |
| 75% | 1.000000 | 7.000000 | 3.000000 | 1.000000 | 15.566667 | 162.250000 | 52.000000 | 40.000000 | 182.000000 | 34.000000 | 32.00000 | 258.000000 | 580.250000 | 382.000000 | 732.250000 | 20.000000 | 455.000000 | 75.250000 | 49.000000 | 475.000000 | 50.000000 | 48.000000 | 2.000000 | 8.000000 | 3.000000 | 1.000000 | 16.941667 | 162.000000 | 56.000000 | 43.000000 | 185.250000 | 37.000000 | 38.000000 | 257.000000 | 585.000000 | 391.000000 | 726.000000 | 21.000000 | 453.000000 | 81.000000 | 53.000000 | 473.250000 | 54.000000 | 54.000000 | 13.000000 | 292.000000 | 25.000000 | 10.000000 | 290.250000 | 16.000000 | 17.000000 | 324.000000 | 340.500000 | 0.500000 | 0.412429 | 0.781250 | 0.904304 | 0.438225 | 0.785714 | 0.797258 | 0.501828 | 0.605620 | 0.736842 | 0.679482 | 0.371665 | 0.253333 | 0.665761 | 0.388589 | 0.383721 | 0.589147 | 0.513450 | 62.580924 | 70.975399 | 74.126025 | 69.486405 | 70.608546 | 69.804704 | 70.608546 | 70.845921 | 70.306431 | 70.090634 | 69.788520 | 69.723781 | 69.507984 | 74.589810 | 71.126457 | 70.457488 | 71.385412 | 70.241692 | 70.274061 | 70.543807 | 72.637031 | 70.392749 | 70.220112 |
| max | 18.000000 | 84.000000 | 48.000000 | 13.000000 | 156.500000 | 1136.000000 | 594.000000 | 425.000000 | 1380.000000 | 453.000000 | 332.00000 | 1896.000000 | 3783.000000 | 2525.000000 | 4550.000000 | 231.000000 | 2872.000000 | 817.000000 | 559.000000 | 3392.000000 | 576.000000 | 429.000000 | 10.000000 | 59.000000 | 29.000000 | 12.000000 | 128.183333 | 1066.000000 | 414.000000 | 264.000000 | 1434.000000 | 259.000000 | 268.000000 | 1721.000000 | 4464.000000 | 2273.000000 | 5118.000000 | 131.000000 | 3475.000000 | 672.000000 | 317.000000 | 3940.000000 | 417.000000 | 389.000000 | 102.000000 | 2426.000000 | 258.000000 | 82.000000 | 2557.000000 | 162.000000 | 127.000000 | 2743.000000 | 2845.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.868852 | 99.935261 | 99.956841 | 100.000000 | 100.000000 | 99.956841 | 99.784204 | 99.956841 | 99.956841 | 99.827363 | 100.000000 | 99.741044 | 99.956841 | 99.870522 | 99.827288 | 99.827363 | 99.913681 | 99.956841 | 99.697885 | 99.956841 | 100.000000 | 99.784204 | 99.956841 | 99.956841 |
# List of columns to calculate means
columns_of_interest = [
"KD_PERCENTILE", "TD_PERCENTILE", "SUB.ATT_PERCENTILE", "REV_PERCENTILE", "CTRL_PERCENTILE",
"HEAD_PERCENTILE", "BODY_PERCENTILE", "LEG_PERCENTILE", "DISTANCE_PERCENTILE", "CLINCH_PERCENTILE",
"GROUND_PERCENTILE", "SIG_PERCENTILE", "STR_PERCENTILE", "STR_%_PERCENTILE", "TD_DEF_PERCENTILE",
"HEAD_DEF_PERCENTILE", "BODY_DEF_PERCENTILE", "LEG_DEF_PERCENTILE", "DISTANCE_DEF_PERCENTILE",
"CLINCH_DEF_PERCENTILE", "GROUND_DEF_PERCENTILE", "SIG_DEF_PERCENTILE", "STR_DEF_PERCENTILE"
]
# Calculate the mean for each of the specified columns
means4 = c_non_challengers[columns_of_interest].mean()
# Rank the means from highest to lowest and plot again
sorted_means4 = means4.sort_values(ascending=False)
# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_means4.plot(kind='bar', color='khaki')
plt.title('Mean Percentiles across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90) # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()
# Display max rows
pd.set_option('display.max_rows', None)
print(sorted_means4)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)
STR_%_PERCENTILE 49.425157 REV_PERCENTILE 48.657049 SUB.ATT_PERCENTILE 47.736600 GROUND_DEF_PERCENTILE 47.735251 BODY_DEF_PERCENTILE 47.362220 TD_PERCENTILE 47.342288 KD_PERCENTILE 47.292138 TD_DEF_PERCENTILE 47.273533 LEG_DEF_PERCENTILE 47.257246 LEG_PERCENTILE 47.230374 CLINCH_DEF_PERCENTILE 47.182211 DISTANCE_PERCENTILE 47.107032 CTRL_PERCENTILE 47.041820 BODY_PERCENTILE 47.033542 DISTANCE_DEF_PERCENTILE 46.975671 CLINCH_PERCENTILE 46.936187 SIG_DEF_PERCENTILE 46.888734 STR_DEF_PERCENTILE 46.879159 HEAD_DEF_PERCENTILE 46.876833 SIG_PERCENTILE 46.769140 HEAD_PERCENTILE 46.740683 GROUND_PERCENTILE 46.734743 STR_PERCENTILE 46.609702 dtype: float64
# List of columns to calculate means
columns_of_interest = [
"TD_%", 'HEAD_%', 'BODY_%', 'LEG_%', 'DISTANCE_%', 'CLINCH_%', 'GROUND_%', 'SIG_%', 'STR_%', 'TD_DEF_%',
'HEAD_DEF_%', 'BODY_DEF_%', 'LEG_DEF_%', 'DISTANCE_DEF_%', 'CLINCH_DEF_%', 'GROUND_DEF_%', 'SIG_DEF_%', 'STR_DEF_%'
]
# Calculate the mean for each of the specified columns
meansD = c_non_challengers[columns_of_interest].mean()
# Rank the means from highest to lowest and plot again
sorted_meansD = meansD.sort_values(ascending=False)
# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_meansD.plot(kind='bar', color='indianred')
plt.title('Mean Percentiles across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90) # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()
# Display max rows
pd.set_option('display.max_rows', None)
print(sorted_meansD)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)
LEG_% 0.794597 BODY_% 0.683110 GROUND_% 0.677831 CLINCH_% 0.664954 DISTANCE_DEF_% 0.604814 HEAD_DEF_% 0.604784 TD_DEF_% 0.554721 STR_% 0.528742 SIG_DEF_% 0.522517 SIG_% 0.434040 STR_DEF_% 0.433401 DISTANCE_% 0.370691 TD_% 0.363409 HEAD_% 0.341382 CLINCH_DEF_% 0.303448 GROUND_DEF_% 0.297266 BODY_DEF_% 0.289315 LEG_DEF_% 0.188770 dtype: float64
fight_average.describe()
| FA_KD | FA_TD | FA_SUB.ATT | FA_REV | FA_CTRL | FA_HEAD | FA_BODY | FA_LEG | FA_DISTANCE | FA_CLINCH | FA_GROUND | FA_SIG | FA_TOT.SIG | FA_STR | FA_TOTAL.STR | FA_TOTAL.TD | FA_TOTAL.HEAD | FA_TOTAL.BODY | FA_TOTAL.LEG | FA_TOTAL.DISTANCE | FA_TOTAL.CLINCH | FA_TOTAL.GROUND | FA_OPP_KD | FA_OPP_TD | FA_OPP_SUB.ATT | FA_OPP_REV | FA_OPP_CTRL | FA_OPP_HEAD | FA_OPP_BODY | FA_OPP_LEG | FA_OPP_DISTANCE | FA_OPP_CLINCH | FA_OPP_GROUND | FA_OPP_SIG | FA_OPP_TOT.SIG | FA_OPP_STR | FA_OPP_TOTAL.STR | FA_OPP_TOTAL.TD | FA_OPP_TOTAL.HEAD | FA_OPP_TOTAL.BODY | FA_OPP_TOTAL.LEG | FA_OPP_TOTAL.DISTANCE | FA_OPP_TOTAL.CLINCH | FA_OPP_TOTAL.GROUND | FA_TD_DEF | FA_HEAD_DEF | FA_BODY_DEF | FA_LEG_DEF | FA_DISTANCE_DEF | FA_CLINCH_DEF | FA_GROUND_DEF | FA_SIG_DEF | FA_STR_DEF | FA_TD_% | FA_HEAD_% | FA_BODY_% | FA_LEG_% | FA_DISTANCE_% | FA_CLINCH_% | FA_GROUND_% | FA_SIG_% | FA_STR_% | FA_TD_DEF_% | FA_HEAD_DEF_% | FA_BODY_DEF_% | FA_LEG_DEF_% | FA_DISTANCE_DEF_% | FA_CLINCH_DEF_% | FA_GROUND_DEF_% | FA_SIG_DEF_% | FA_STR_DEF_% | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2085.000000 | 2306.000000 | 2229.000000 | 2191.000000 | 2308.000000 | 2176.000000 | 1977.000000 | 2313.000000 | 2316.000000 | 2130.000000 | 2311.000000 | 2247.000000 | 2188.000000 | 2307.000000 | 2185.000000 | 2162.000000 | 2314.000000 | 2315.000000 |
| mean | 0.102005 | 0.396189 | 0.177114 | 0.057451 | 0.828589 | 8.017597 | 2.657430 | 2.153952 | 9.093827 | 1.894024 | 1.841128 | 12.828979 | 29.160414 | 19.493507 | 36.800267 | 1.114328 | 22.583911 | 3.863829 | 2.712673 | 23.697608 | 2.770589 | 2.692217 | 0.157146 | 0.493745 | 0.248937 | 0.060147 | 1.033310 | 9.644342 | 2.930910 | 2.258015 | 9.783036 | 2.182145 | 2.868086 | 14.833268 | 31.625764 | 22.865865 | 40.828685 | 1.143396 | 24.696713 | 4.127037 | 2.802014 | 24.379967 | 3.094110 | 4.151687 | 0.649651 | 15.052371 | 1.196127 | 0.543998 | 14.596930 | 0.911965 | 1.283601 | 16.792496 | 17.962820 | 0.374723 | 0.349601 | 0.687569 | 0.795446 | 0.373780 | 0.670462 | 0.682669 | 0.440162 | 0.532381 | 0.558849 | 0.603835 | 0.289909 | 0.190255 | 0.604862 | 0.302909 | 0.293672 | 0.522411 | 0.435965 |
| std | 0.158727 | 0.407920 | 0.276405 | 0.114033 | 0.671398 | 4.857685 | 1.922465 | 1.803111 | 6.129980 | 1.674394 | 2.248926 | 6.915715 | 14.595553 | 9.171340 | 15.871864 | 0.966141 | 11.942925 | 2.677142 | 2.241967 | 14.067329 | 2.312388 | 3.253183 | 0.231004 | 0.455493 | 0.385061 | 0.131657 | 0.772006 | 5.057907 | 1.991133 | 1.757561 | 6.369895 | 2.051110 | 3.295818 | 6.781977 | 14.033882 | 9.551450 | 15.045011 | 0.844002 | 11.557293 | 2.634654 | 2.143185 | 14.365358 | 2.737444 | 4.811363 | 0.590317 | 7.853677 | 0.938833 | 0.612955 | 8.742281 | 0.919348 | 1.784963 | 8.580392 | 8.568811 | 0.254084 | 0.122528 | 0.174160 | 0.171992 | 0.118646 | 0.192223 | 0.201262 | 0.119060 | 0.129456 | 0.260741 | 0.117977 | 0.154262 | 0.155821 | 0.110411 | 0.167786 | 0.166646 | 0.102900 | 0.115474 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.066667 | 0.000000 | 0.000000 | 0.308565 | 4.500000 | 1.277778 | 0.850000 | 4.500000 | 0.729167 | 0.333333 | 7.952381 | 19.000000 | 13.625000 | 26.666667 | 0.363333 | 14.211111 | 1.952381 | 1.050000 | 13.153846 | 1.166667 | 0.500000 | 0.000000 | 0.166667 | 0.000000 | 0.000000 | 0.473611 | 6.300000 | 1.600000 | 1.000000 | 5.160000 | 0.916667 | 0.863636 | 10.166667 | 22.080952 | 16.900000 | 31.111111 | 0.566667 | 16.861111 | 2.333333 | 1.333333 | 14.166667 | 1.333333 | 1.219444 | 0.230769 | 9.583333 | 0.541667 | 0.125000 | 8.416667 | 0.333333 | 0.250000 | 10.833333 | 12.000000 | 0.200000 | 0.285714 | 0.608696 | 0.729079 | 0.313344 | 0.589991 | 0.596774 | 0.377717 | 0.457627 | 0.400000 | 0.540541 | 0.202106 | 0.088062 | 0.546983 | 0.202899 | 0.193548 | 0.465675 | 0.367468 |
| 50% | 0.000000 | 0.300000 | 0.083333 | 0.000000 | 0.700000 | 7.605333 | 2.333333 | 1.806250 | 8.186667 | 1.500000 | 1.285714 | 12.333333 | 28.000000 | 18.916667 | 35.833333 | 0.952381 | 21.500000 | 3.437500 | 2.243137 | 22.106061 | 2.266667 | 1.888889 | 0.071429 | 0.391304 | 0.119048 | 0.000000 | 0.885897 | 8.833333 | 2.658333 | 2.000000 | 8.904167 | 1.666667 | 1.944444 | 14.015152 | 30.222727 | 21.466667 | 39.508333 | 1.000000 | 23.316667 | 3.783333 | 2.439394 | 22.722222 | 2.500000 | 2.791667 | 0.533333 | 13.928571 | 1.029412 | 0.404762 | 13.481481 | 0.708333 | 0.744444 | 15.666667 | 16.888889 | 0.368421 | 0.355643 | 0.700000 | 0.821429 | 0.379855 | 0.692308 | 0.697674 | 0.442922 | 0.533225 | 0.595865 | 0.619916 | 0.288136 | 0.171793 | 0.607275 | 0.294118 | 0.291253 | 0.531250 | 0.445578 |
| 75% | 0.166667 | 0.592857 | 0.250000 | 0.076923 | 1.208333 | 10.717949 | 3.605556 | 3.000000 | 12.533333 | 2.607143 | 2.622222 | 17.000000 | 38.500000 | 24.833333 | 46.764706 | 1.625000 | 29.762222 | 5.277778 | 3.791667 | 32.416667 | 3.777778 | 3.729167 | 0.222222 | 0.694444 | 0.333333 | 0.083333 | 1.428056 | 12.166667 | 3.864444 | 3.000000 | 13.214286 | 2.904762 | 3.583333 | 18.500000 | 39.444444 | 27.470833 | 49.333333 | 1.555556 | 31.250000 | 5.416667 | 3.825397 | 32.688095 | 4.041667 | 5.333333 | 0.930556 | 19.491667 | 1.666667 | 0.777778 | 19.666667 | 1.183333 | 1.571429 | 21.666667 | 22.854762 | 0.500000 | 0.423267 | 0.785714 | 0.904762 | 0.440638 | 0.786593 | 0.796610 | 0.509091 | 0.607611 | 0.731799 | 0.678351 | 0.372035 | 0.255910 | 0.662386 | 0.386777 | 0.378378 | 0.586715 | 0.514752 |
| max | 1.000000 | 3.500000 | 3.000000 | 1.500000 | 4.891667 | 35.000000 | 15.666667 | 21.000000 | 45.333333 | 15.000000 | 32.000000 | 49.666667 | 98.333333 | 75.000000 | 105.000000 | 7.333333 | 80.333333 | 18.777778 | 24.000000 | 95.500000 | 20.166667 | 49.000000 | 2.000000 | 5.000000 | 5.000000 | 2.000000 | 4.700000 | 38.500000 | 21.000000 | 20.500000 | 54.000000 | 20.500000 | 28.000000 | 60.000000 | 100.000000 | 85.333333 | 112.000000 | 7.333333 | 82.750000 | 30.833333 | 24.500000 | 98.333333 | 30.500000 | 39.000000 | 6.000000 | 72.000000 | 11.833333 | 9.000000 | 73.000000 | 12.000000 | 19.000000 | 73.333333 | 73.333333 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.868852 |
metrics1 = ['FA_KD', 'FA_TD', 'FA_SUB.ATT', 'FA_REV', 'FA_CTRL', 'FA_HEAD', 'FA_BODY', 'FA_LEG', 'FA_DISTANCE',
'FA_CLINCH', 'FA_GROUND', 'FA_SIG', 'FA_STR']
metrics2 = [
'FA_TD_%','FA_HEAD_%','FA_BODY_%','FA_LEG_%','FA_DISTANCE_%','FA_CLINCH_%', 'FA_GROUND_%', 'FA_SIG_%',
'FA_STR_%', 'FA_TD_DEF_%','FA_HEAD_DEF_%','FA_BODY_DEF_%','FA_LEG_DEF_%', 'FA_DISTANCE_DEF_%',
'FA_CLINCH_DEF_%', 'FA_GROUND_DEF_%', 'FA_SIG_DEF_%', 'FA_STR_DEF_%']
# Filter the dataframe using the correct column name "FIGHTER"
fa = fight_average[fight_average['FIGHTER'].isin(fighters_list)]
fa
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | FA_FIGHTER | FA_KD | FA_TD | FA_SUB.ATT | FA_REV | FA_CTRL | FA_HEAD | FA_BODY | FA_LEG | FA_DISTANCE | FA_CLINCH | FA_GROUND | FA_SIG | FA_TOT.SIG | FA_STR | FA_TOTAL.STR | FA_TOTAL.TD | FA_TOTAL.HEAD | FA_TOTAL.BODY | FA_TOTAL.LEG | FA_TOTAL.DISTANCE | FA_TOTAL.CLINCH | FA_TOTAL.GROUND | FA_OPP_KD | FA_OPP_TD | FA_OPP_SUB.ATT | FA_OPP_REV | FA_OPP_CTRL | FA_OPP_HEAD | FA_OPP_BODY | FA_OPP_LEG | FA_OPP_DISTANCE | FA_OPP_CLINCH | FA_OPP_GROUND | FA_OPP_SIG | FA_OPP_TOT.SIG | FA_OPP_STR | FA_OPP_TOTAL.STR | FA_OPP_TOTAL.TD | FA_OPP_TOTAL.HEAD | FA_OPP_TOTAL.BODY | FA_OPP_TOTAL.LEG | FA_OPP_TOTAL.DISTANCE | FA_OPP_TOTAL.CLINCH | FA_OPP_TOTAL.GROUND | FA_TD_DEF | FA_HEAD_DEF | FA_BODY_DEF | FA_LEG_DEF | FA_DISTANCE_DEF | FA_CLINCH_DEF | FA_GROUND_DEF | FA_SIG_DEF | FA_STR_DEF | FA_TD_% | FA_HEAD_% | FA_BODY_% | FA_LEG_% | FA_DISTANCE_% | FA_CLINCH_% | FA_GROUND_% | FA_SIG_% | FA_STR_% | FA_TD_DEF_% | FA_HEAD_DEF_% | FA_BODY_DEF_% | FA_LEG_DEF_% | FA_DISTANCE_DEF_% | FA_CLINCH_DEF_% | FA_GROUND_DEF_% | FA_SIG_DEF_% | FA_STR_DEF_% | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | CJ Vergara | Orthodox | No | No | No | No | CJ Vergara | 0.000000 | 0.000000 | 0.083333 | 0.083333 | 0.694444 | 13.222222 | 8.861111 | 3.194444 | 17.111111 | 2.027778 | 6.138889 | 25.277778 | 43.722222 | 35.694444 | 55.388889 | 0.000000 | 28.388889 | 11.694444 | 3.638889 | 31.944444 | 2.694444 | 9.083333 | 0.166667 | 1.000000 | 0.388889 | 0.111111 | 1.316204 | 13.611111 | 5.500000 | 2.944444 | 19.027778 | 1.527778 | 1.500000 | 22.055556 | 46.583333 | 26.611111 | 52.277778 | 2.500000 | 36.055556 | 7.333333 | 3.194444 | 42.500000 | 2.083333 | 2.000000 | 1.500000 | 22.444444 | 1.833333 | 0.250000 | 23.472222 | 0.555556 | 0.500000 | 24.527778 | 25.666667 | NaN | 0.465753 | 0.757720 | 0.877863 | 0.535652 | 0.752577 | 0.675841 | 0.578145 | 0.644433 | 0.600000 | 0.622496 | 0.250000 | 0.078261 | 0.552288 | 0.266667 | 0.250000 | 0.526535 | 0.490967 |
| 2 | Curtis Blaydes | Orthodox | No | No | No | No | Curtis Blaydes | 0.055556 | 1.266667 | 0.000000 | 0.000000 | 1.519198 | 9.433333 | 1.475926 | 2.385185 | 6.462963 | 1.612963 | 5.218519 | 13.294444 | 27.970370 | 22.492593 | 39.153704 | 2.444444 | 23.518519 | 1.692593 | 2.759259 | 19.392593 | 2.029630 | 6.548148 | 0.157407 | 0.344444 | 0.037037 | 0.000000 | 0.201883 | 6.401852 | 1.224074 | 0.727778 | 6.603704 | 1.101852 | 0.648148 | 8.353704 | 19.874074 | 12.364815 | 24.161111 | 0.485185 | 17.364815 | 1.670370 | 0.838889 | 17.751852 | 1.344444 | 0.777778 | 0.140741 | 10.962963 | 0.446296 | 0.111111 | 11.148148 | 0.242593 | 0.129630 | 11.520370 | 11.796296 | 0.518182 | 0.401102 | 0.871991 | 0.864430 | 0.333270 | 0.794708 | 0.796946 | 0.475305 | 0.574469 | 0.290076 | 0.631332 | 0.267184 | 0.132450 | 0.627999 | 0.180441 | 0.166667 | 0.579668 | 0.488235 |
| 3 | Jailton Almeida | Orthodox | No | No | No | No | Jailton Almeida | 0.000000 | 2.028571 | 0.685714 | 0.028571 | 3.565476 | 13.557143 | 0.571429 | 0.100000 | 0.428571 | 0.085714 | 13.714286 | 14.228571 | 22.457143 | 26.142857 | 37.014286 | 3.000000 | 21.228571 | 0.857143 | 0.371429 | 1.771429 | 0.228571 | 20.457143 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.037857 | 1.700000 | 0.085714 | 0.142857 | 0.271429 | 0.028571 | 1.628571 | 1.928571 | 3.400000 | 5.800000 | 8.042857 | 0.057143 | 3.085714 | 0.171429 | 0.142857 | 1.142857 | 0.028571 | 2.228571 | 0.057143 | 1.385714 | 0.085714 | 0.000000 | 0.871429 | 0.000000 | 0.600000 | 1.471429 | 2.242857 | 0.676190 | 0.638627 | 0.666667 | 0.269231 | 0.241935 | 0.375000 | 0.670391 | 0.633588 | 0.706291 | 1.000000 | 0.449074 | 0.500000 | 0.000000 | 0.762500 | 0.000000 | 0.269231 | 0.432773 | 0.278863 |
| 4 | Benoit Saint Denis | Southpaw | No | No | No | No | Benoit Saint Denis | 0.357143 | 1.238095 | 0.500000 | 0.142857 | 1.931746 | 10.833333 | 6.380952 | 3.047619 | 11.404762 | 3.642857 | 5.214286 | 20.261905 | 37.119048 | 32.666667 | 51.357143 | 3.238095 | 25.642857 | 8.285714 | 3.190476 | 23.309524 | 4.690476 | 9.119048 | 0.071429 | 0.428571 | 0.428571 | 0.000000 | 0.255952 | 9.857143 | 4.261905 | 1.619048 | 11.976190 | 3.238095 | 0.523810 | 15.738095 | 27.547619 | 18.952381 | 31.571429 | 0.976190 | 21.023810 | 4.785714 | 1.738095 | 22.642857 | 4.380952 | 0.523810 | 0.547619 | 11.166667 | 0.523810 | 0.119048 | 10.666667 | 1.142857 | 0.000000 | 11.809524 | 12.619048 | 0.382353 | 0.422470 | 0.770115 | 0.955224 | 0.489275 | 0.776650 | 0.571802 | 0.545863 | 0.636069 | 0.560976 | 0.531144 | 0.109453 | 0.068493 | 0.471083 | 0.260870 | 0.000000 | 0.428695 | 0.399698 |
| 5 | Dustin Poirier | Southpaw | No | Yes | Yes | Yes | Dustin Poirier | 0.333333 | 0.380000 | 0.415556 | 0.038889 | 0.931787 | 18.404444 | 1.986667 | 2.854444 | 16.203889 | 4.217222 | 2.824444 | 23.245556 | 45.263333 | 27.461667 | 50.016111 | 0.986111 | 38.940556 | 2.903333 | 3.419444 | 34.181111 | 6.597778 | 4.484444 | 0.083333 | 0.337778 | 0.337222 | 0.033333 | 0.737278 | 11.157222 | 3.819444 | 2.393889 | 12.997778 | 2.606667 | 1.766111 | 17.370556 | 38.315556 | 22.536111 | 44.076111 | 1.026667 | 29.803889 | 5.589444 | 2.922222 | 32.151667 | 3.980000 | 2.183889 | 0.688889 | 18.646667 | 1.770000 | 0.528333 | 19.153889 | 1.373333 | 0.417778 | 20.945000 | 21.540000 | 0.385352 | 0.472629 | 0.684271 | 0.834768 | 0.474060 | 0.639188 | 0.629832 | 0.513563 | 0.549056 | 0.670996 | 0.625645 | 0.316668 | 0.180798 | 0.595735 | 0.345059 | 0.191300 | 0.546645 | 0.488700 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2252 | Ian Freeman | Orthodox | No | No | No | No | Ian Freeman | 0.000000 | 0.233333 | 0.066667 | 0.200000 | 1.591111 | 9.900000 | 1.366667 | 0.533333 | 1.300000 | 3.500000 | 7.000000 | 11.800000 | 21.266667 | 23.166667 | 33.066667 | 0.433333 | 19.100000 | 1.633333 | 0.533333 | 5.833333 | 4.633333 | 10.800000 | 0.200000 | 0.433333 | 0.600000 | 0.166667 | 1.223333 | 3.666667 | 2.700000 | 3.200000 | 3.866667 | 3.100000 | 2.600000 | 9.566667 | 16.400000 | 15.400000 | 22.700000 | 2.600000 | 9.000000 | 3.366667 | 4.033333 | 8.700000 | 3.900000 | 3.800000 | 2.166667 | 5.333333 | 0.666667 | 0.833333 | 4.833333 | 0.800000 | 1.200000 | 6.833333 | 7.300000 | 0.538462 | 0.518325 | 0.836735 | 1.000000 | 0.222857 | 0.755396 | 0.648148 | 0.554859 | 0.700605 | 0.833333 | 0.592593 | 0.198020 | 0.206612 | 0.555556 | 0.205128 | 0.315789 | 0.416667 | 0.321586 |
| 2264 | Eugene Jackson | Orthodox | No | No | No | No | Eugene Jackson | 0.000000 | 0.500000 | 0.625000 | 0.000000 | 0.166667 | 0.625000 | 0.250000 | 0.000000 | 0.375000 | 0.250000 | 0.250000 | 0.875000 | 3.875000 | 12.750000 | 16.375000 | 0.625000 | 3.375000 | 0.500000 | 0.000000 | 3.125000 | 0.250000 | 0.500000 | 0.250000 | 0.750000 | 0.750000 | 0.000000 | 2.368750 | 1.500000 | 1.125000 | 0.625000 | 1.500000 | 0.000000 | 1.750000 | 3.250000 | 5.250000 | 13.375000 | 16.125000 | 1.000000 | 2.750000 | 1.125000 | 1.375000 | 2.500000 | 0.000000 | 2.750000 | 0.250000 | 1.250000 | 0.000000 | 0.750000 | 1.000000 | 0.000000 | 1.000000 | 2.000000 | 2.750000 | 0.800000 | 0.185185 | 0.500000 | NaN | 0.120000 | 1.000000 | 0.500000 | 0.225806 | 0.778626 | 0.250000 | 0.454545 | 0.000000 | 0.545455 | 0.400000 | NaN | 0.363636 | 0.380952 | 0.170543 |
| 2278 | Pat Miletich | Orthodox | Yes | No | Yes | Yes | Pat Miletich | 0.100000 | 0.600000 | 0.300000 | 0.000000 | 1.392222 | 4.166667 | 0.566667 | 0.833333 | 3.300000 | 0.800000 | 1.466667 | 5.566667 | 11.666667 | 18.633333 | 24.966667 | 0.600000 | 9.966667 | 0.666667 | 1.033333 | 8.633333 | 1.200000 | 1.833333 | 0.000000 | 0.466667 | 0.266667 | 0.000000 | 0.869444 | 4.366667 | 1.366667 | 1.000000 | 1.800000 | 0.866667 | 4.066667 | 6.733333 | 16.566667 | 18.933333 | 29.800000 | 1.000000 | 13.800000 | 1.566667 | 1.200000 | 7.466667 | 1.100000 | 8.000000 | 0.533333 | 9.433333 | 0.200000 | 0.200000 | 5.666667 | 0.233333 | 3.933333 | 9.833333 | 10.866667 | 1.000000 | 0.418060 | 0.850000 | 0.806452 | 0.382239 | 0.666667 | 0.800000 | 0.477143 | 0.746328 | 0.533333 | 0.683575 | 0.127660 | 0.166667 | 0.758929 | 0.212121 | 0.491667 | 0.593561 | 0.364653 |
| 2282 | Kevin Randleman | Orthodox | Yes | No | Yes | Yes | Kevin Randleman | 0.000000 | 0.350000 | 0.183333 | 0.000000 | 2.147778 | 3.016667 | 0.500000 | 0.083333 | 0.800000 | 0.600000 | 2.200000 | 3.600000 | 7.433333 | 11.816667 | 16.333333 | 0.950000 | 6.850000 | 0.500000 | 0.083333 | 2.533333 | 0.700000 | 4.200000 | 0.250000 | 0.083333 | 0.500000 | 0.000000 | 0.254167 | 2.150000 | 0.916667 | 0.783333 | 1.100000 | 1.500000 | 1.250000 | 3.850000 | 7.766667 | 9.716667 | 13.900000 | 0.083333 | 5.883333 | 1.000000 | 0.883333 | 4.250000 | 1.716667 | 1.800000 | 0.000000 | 3.733333 | 0.083333 | 0.100000 | 3.150000 | 0.216667 | 0.550000 | 3.916667 | 4.183333 | 0.368421 | 0.440389 | 1.000000 | 1.000000 | 0.315789 | 0.857143 | 0.523810 | 0.484305 | 0.723469 | 0.000000 | 0.634561 | 0.083333 | 0.113208 | 0.741176 | 0.126214 | 0.305556 | 0.504292 | 0.300959 |
| 2286 | Fabiano Iha | Orthodox | No | No | No | No | Fabiano Iha | 0.000000 | 0.733333 | 0.933333 | 0.000000 | 0.961111 | 3.200000 | 0.466667 | 1.200000 | 1.866667 | 1.133333 | 1.866667 | 4.866667 | 9.800000 | 8.466667 | 13.800000 | 1.266667 | 7.600000 | 0.800000 | 1.400000 | 4.933333 | 2.000000 | 2.866667 | 0.000000 | 0.466667 | 0.000000 | 0.000000 | 0.723333 | 4.333333 | 0.266667 | 0.400000 | 1.533333 | 1.800000 | 1.666667 | 5.000000 | 9.400000 | 10.933333 | 15.733333 | 1.066667 | 8.533333 | 0.266667 | 0.600000 | 4.000000 | 2.733333 | 2.666667 | 0.600000 | 4.200000 | 0.000000 | 0.200000 | 2.466667 | 0.933333 | 1.000000 | 4.400000 | 4.800000 | 0.578947 | 0.421053 | 0.583333 | 0.857143 | 0.378378 | 0.566667 | 0.651163 | 0.496599 | 0.613527 | 0.562500 | 0.492188 | 0.000000 | 0.333333 | 0.616667 | 0.341463 | 0.375000 | 0.468085 | 0.305085 |
1321 rows × 78 columns
# Original metrics list
original_metrics = [
"KD", "TD", "SUB.ATT", "REV", "CTRL", "HEAD", "BODY", "LEG", "DISTANCE",
"CLINCH", "GROUND", "SIG", "STR", "TD_DEF", "HEAD_DEF", "BODY_DEF", "LEG_DEF",
"DISTANCE_DEF", "CLINCH_DEF", "GROUND_DEF", "SIG_DEF", "STR_DEF", "TD_%",
"HEAD_%", "BODY_%", "LEG_%", "DISTANCE_%", "CLINCH_%", "GROUND_%", "SIG_%",
"STR_%", "TD_DEF_%", "HEAD_DEF_%", "BODY_DEF_%", "LEG_DEF_%", "DISTANCE_DEF_%",
"CLINCH_DEF_%", "GROUND_DEF_%", "SIG_DEF_%", "STR_DEF_%"
]
# Adding 'FA_' prefix to each metric in the list
fa_metrics = ["FA_" + metric for metric in original_metrics]
fa_metrics
['FA_KD', 'FA_TD', 'FA_SUB.ATT', 'FA_REV', 'FA_CTRL', 'FA_HEAD', 'FA_BODY', 'FA_LEG', 'FA_DISTANCE', 'FA_CLINCH', 'FA_GROUND', 'FA_SIG', 'FA_STR', 'FA_TD_DEF', 'FA_HEAD_DEF', 'FA_BODY_DEF', 'FA_LEG_DEF', 'FA_DISTANCE_DEF', 'FA_CLINCH_DEF', 'FA_GROUND_DEF', 'FA_SIG_DEF', 'FA_STR_DEF', 'FA_TD_%', 'FA_HEAD_%', 'FA_BODY_%', 'FA_LEG_%', 'FA_DISTANCE_%', 'FA_CLINCH_%', 'FA_GROUND_%', 'FA_SIG_%', 'FA_STR_%', 'FA_TD_DEF_%', 'FA_HEAD_DEF_%', 'FA_BODY_DEF_%', 'FA_LEG_DEF_%', 'FA_DISTANCE_DEF_%', 'FA_CLINCH_DEF_%', 'FA_GROUND_DEF_%', 'FA_SIG_DEF_%', 'FA_STR_DEF_%']
# Reinitializing the dictionary to hold the top ten fighters for the updated list of metrics
top_fighters_per_metric = {}
# Creating a dictionary to hold the top ten fighters for each metric
top_fighters_per_metric = {}
# Finding the top ten fighters for each metric
for metric in fa_metrics:
# Some metrics may have missing values, so we sort only non-NA values
top_fighters_per_metric[metric] = fa.nlargest(10, metric)[['FIGHTER', metric]]
# Printing the top ten fighters for each metric with a blank line in between
for metric, data in top_fighters_per_metric.items():
print(f"Top ten fighters for {metric}:")
if isinstance(data, pd.DataFrame):
print(data.to_string(index=False))
else:
print(data)
print("\n") # Blank line for separation
Top ten fighters for FA_KD:
FIGHTER FA_KD
Sergei Pavlovich 0.875000
Jack Della Maddalena 0.761905
Danaa Batgerel 0.750000
Steve Garcia 0.666667
Todd Duffee 0.666667
Drew McFedries 0.666667
Punahele Soriano 0.642857
Abdul Razak Alhassan 0.625000
Chris Daukaus 0.625000
Albert Tumenov 0.625000
Top ten fighters for FA_TD:
FIGHTER FA_TD
Merab Dvalishvili 2.127778
Jailton Almeida 2.028571
Tatiana Suarez 1.880952
Jacob Malkoun 1.833333
Jeremy Kennedy 1.750000
Zabit Magomedsharipov 1.694444
Muhammad Mokaev 1.666667
Gregor Gillespie 1.645833
Karo Parisyan 1.628205
Serghei Spivac 1.590909
Top ten fighters for FA_SUB.ATT:
FIGHTER FA_SUB.ATT
James Wilks 2.333333
Paul Sass 2.133333
John Albert 1.500000
Shannon Gugerty 1.166667
TJ Waldburger 1.125000
Khamzat Chimaev 1.083333
Ramiz Brahimaj 1.083333
Dustin Hazelett 1.083333
Rousimar Palhares 1.027778
Rob Kimmons 0.952381
Top ten fighters for FA_REV:
FIGHTER FA_REV
Cameron Saaiman 0.750000
Yosdenis Cedeno 0.583333
Alvin Robinson 0.500000
Michinori Tanaka 0.466667
Jerrod Sanders 0.458333
Cody McKenzie 0.428571
Dan Argueta 0.416667
Luis Pena 0.416667
Veronica Macedo 0.400000
Heath Herring 0.400000
Top ten fighters for FA_CTRL:
FIGHTER FA_CTRL
Jailton Almeida 3.565476
Tatsuro Taira 2.877222
Tatiana Suarez 2.849603
Rinat Fakhretdinov 2.716667
Chael Sonnen 2.713611
Joe Solecki 2.692460
Gregor Gillespie 2.659375
Grant Dawson 2.611111
Jacob Malkoun 2.593519
Matt Lindland 2.550926
Top ten fighters for FA_HEAD:
FIGHTER FA_HEAD
Casey O'Neill 27.583333
Billy Quarantillo 22.962963
Sean Strickland 22.714286
Max Holloway 22.256790
Lavar Johnson 22.250000
David Onama 21.766667
Daniel Rodriguez 21.650000
Shane Burgos 20.909091
Leslie Smith 20.880952
Michael Morales 20.583333
Top ten fighters for FA_BODY:
FIGHTER FA_BODY
Kai Kamaka 10.250000
Tony Kelley 10.208333
Paulo Costa 9.544444
CJ Vergara 8.861111
Loma Lookboonmee 8.750000
John Lineker 8.666667
Michel Pereira 8.333333
Cristiane Justino 8.304762
Diana Belbita 8.142857
Joselyne Edwards 8.095238
Top ten fighters for FA_LEG:
FIGHTER FA_LEG
Armen Petrosyan 11.666667
Chris Gutierrez 11.169444
Marcelo Guimaraes 10.583333
Joanne Wood 10.215686
Nathaniel Wood 9.851852
Marcin Prachnio 8.870370
Karol Rosa 8.777778
Sarah Kaufman 8.750000
Justin Gaethje 8.180556
Emily Ducote 7.833333
Top ten fighters for FA_DISTANCE:
FIGHTER FA_DISTANCE
Emily Ducote 33.333333
Ignacio Bahamondes 30.200000
Daniel Rodriguez 29.483333
Waldo Cortes-Acosta 28.333333
Sean Woodson 27.809524
Sean O'Malley 27.297222
Max Holloway 27.126543
Casey O'Neill 26.972222
Shane Burgos 26.909091
Ian Garry 25.611111
Top ten fighters for FA_CLINCH:
FIGHTER FA_CLINCH
Lavar Johnson 14.166667
Sarah Kaufman 12.375000
Leslie Smith 8.571429
Cody Donovan 8.375000
Timothy Johnson 8.142857
Maycee Barber 7.954545
Isaac Vallie-Flagg 7.916667
Marc-Andre Barriault 7.777778
Jessica Aguilar 7.600000
Loma Lookboonmee 7.458333
Top ten fighters for FA_GROUND:
FIGHTER FA_GROUND
Khamzat Chimaev 15.055556
Jailton Almeida 13.714286
Cain Velasquez 11.711111
Khabib Nurmagomedov 10.901282
Gregor Gillespie 10.062500
Matt Grice 9.761905
Tatiana Suarez 9.571429
Zhang Weili 9.133333
Brock Lesnar 8.979167
Erin Blanchfield 8.933333
Top ten fighters for FA_SIG:
FIGHTER FA_SIG
Casey O'Neill 35.277778
Emily Ducote 34.666667
Sarah Kaufman 34.458333
Shane Burgos 32.545455
Billy Quarantillo 32.388889
Ignacio Bahamondes 32.333333
Max Holloway 32.283951
Karol Rosa 31.703704
Daniel Rodriguez 31.183333
Joanne Wood 30.607843
Top ten fighters for FA_STR:
FIGHTER FA_STR
Casey O'Neill 54.500000
Merab Dvalishvili 52.450000
Sarah Kaufman 51.583333
Billy Quarantillo 50.407407
Erin Blanchfield 50.300000
Karol Rosa 50.037037
Cain Velasquez 48.120000
Jasmine Jasudavicius 45.777778
Nick Diaz 45.604444
Pascal Krauss 44.833333
Top ten fighters for FA_TD_DEF:
FIGHTER FA_TD_DEF
Charles Johnson 2.904762
Michael Kuiper 2.833333
Diego Nunes 2.722222
Taylor Lapilus 2.694444
Austin Lingo 2.300000
Gasan Umalatov 2.250000
Joanna Jedrzejczyk 2.215556
Shane Campbell 2.200000
Sean Woodson 2.190476
Ian Freeman 2.166667
Top ten fighters for FA_HEAD_DEF:
FIGHTER FA_HEAD_DEF
Nam Phan 54.416667
Emily Ducote 48.416667
Vanessa Melo 41.333333
Irene Aldana 38.087179
Jodie Esquibel 37.666667
Danielle Taylor 37.266667
Nasrat Haqparast 37.125000
Alptekin Ozkilic 36.083333
Mike Davis 35.875000
Shane Burgos 35.060606
Top ten fighters for FA_BODY_DEF:
FIGHTER FA_BODY_DEF
Trey Ogden 5.750000
Luana Pinheiro 4.833333
Sean Strickland 4.649206
Ji Yeon Kim 4.550000
Austin Lingo 4.533333
Nam Phan 4.458333
Manel Kape 4.222222
Casey Kenney 4.083333
Andrea Lee 3.972222
Sean Woodson 3.904762
Top ten fighters for FA_LEG_DEF:
FIGHTER FA_LEG_DEF
Luana Pinheiro 3.583333
Poliana Botelho 2.904762
Seo Hee Ham 2.083333
Danielle Taylor 2.066667
Alatengheili 2.047619
Cody Garbrandt 2.019048
Andre Ewell 2.018519
Daniel Zellhuber 2.000000
Viviane Pereira 2.000000
Hatsu Hioki 2.000000
Top ten fighters for FA_DISTANCE_DEF:
FIGHTER FA_DISTANCE_DEF
Nam Phan 57.750000
Emily Ducote 51.500000
Vanessa Melo 42.666667
Danielle Taylor 41.200000
Irene Aldana 40.835897
Nasrat Haqparast 39.652778
Jodie Esquibel 38.916667
John Makdessi 38.358333
Seo Hee Ham 37.666667
Shane Burgos 37.484848
Top ten fighters for FA_CLINCH_DEF:
FIGHTER FA_CLINCH_DEF
Wesley Correira 6.000000
Stanislav Nedkov 5.208333
Julian Marquez 4.444444
Andre Winner 4.333333
Jonathan Brookins 4.233333
Dominique Steele 4.066667
Alex Chambers 4.066667
Jonathan Wilson 3.916667
Josh Grispi 3.666667
Paul Buentello 3.638889
Top ten fighters for FA_GROUND_DEF:
FIGHTER FA_GROUND_DEF
Edgar Garcia 10.000000
Elvis Sinosic 9.261905
Jason Dent 6.708333
Terrion Ware 6.250000
Germaine de Randamie 6.103704
Seth Petruzelli 6.000000
Jenel Lausa 5.458333
Jeremy Kennedy 5.375000
Danilo Marques 5.166667
Naoyuki Kotani 5.133333
Top ten fighters for FA_SIG_DEF:
FIGHTER FA_SIG_DEF
Nam Phan 59.666667
Emily Ducote 52.083333
Vanessa Melo 44.416667
Irene Aldana 42.543590
Danielle Taylor 41.866667
Jodie Esquibel 40.833333
Seo Hee Ham 40.500000
Alptekin Ozkilic 40.333333
Nasrat Haqparast 39.986111
John Makdessi 39.466667
Top ten fighters for FA_STR_DEF:
FIGHTER FA_STR_DEF
Nam Phan 61.958333
Emily Ducote 52.500000
Vanessa Melo 45.416667
Irene Aldana 43.543590
Danielle Taylor 42.000000
Seo Hee Ham 41.583333
Jodie Esquibel 40.916667
Alptekin Ozkilic 40.666667
Syuri Kondo 40.583333
Nasrat Haqparast 40.486111
Top ten fighters for FA_TD_%:
FIGHTER FA_TD_%
Johnny Walker 1.0
Sumudaerji 1.0
Jared Gooden 1.0
Uros Medic 1.0
Alex Pereira 1.0
Tom Aspinall 1.0
Andre Fialho 1.0
Omar Morales 1.0
Cheyanne Vlismas 1.0
Mariya Agapova 1.0
Top ten fighters for FA_HEAD_%:
FIGHTER FA_HEAD_%
Danilo Marques 0.816794
Tatsuro Taira 0.660944
Umar Nurmagomedov 0.660028
Cyrille Diabate 0.653291
Alistair Overeem 0.649761
Khamzat Chimaev 0.640294
Jailton Almeida 0.638627
Gunnar Nelson 0.631987
Brock Lesnar 0.614583
Manny Bermudez 0.608696
Top ten fighters for FA_BODY_%:
FIGHTER FA_BODY_%
Jeremy Kimball 1.0
Jerrod Sanders 1.0
Paul Sass 1.0
Ken Stone 1.0
Shane Carwin 1.0
Chris Tuchscherer 1.0
Jake O'Brien 1.0
Alvin Robinson 1.0
Rory Singer 1.0
Marcio Cruz 1.0
Top ten fighters for FA_LEG_%:
FIGHTER FA_LEG_%
Nikolas Motta 1.0
Martin Buday 1.0
Steve Garcia 1.0
Joe Solecki 1.0
Uros Medic 1.0
Sergei Pavlovich 1.0
Tom Aspinall 1.0
Nate Maness 1.0
Jacob Malkoun 1.0
Zarah Fairn 1.0
Top ten fighters for FA_DISTANCE_%:
FIGHTER FA_DISTANCE_%
Danilo Marques 0.682927
Cyrille Diabate 0.666168
Alistair Overeem 0.653726
Francisco Figueiredo 0.651515
Tatsuro Taira 0.632479
Umar Nurmagomedov 0.629032
Tom Aspinall 0.625000
Ciryl Gane 0.611429
Sean O'Malley 0.608219
Darrick Minner 0.599138
Top ten fighters for FA_CLINCH_%:
FIGHTER FA_CLINCH_%
Umar Nurmagomedov 1.0
Joe Pyfer 1.0
Mike Malott 1.0
Yohan Lainesse 1.0
Rafael Alves 1.0
Michael Chandler 1.0
Vanessa Melo 1.0
Chance Rencountre 1.0
Alvaro Herrera Mendoza 1.0
Dongi Yang 1.0
Top ten fighters for FA_GROUND_%:
FIGHTER FA_GROUND_%
Marcin Prachnio 1.0
Zac Pauga 1.0
Yohan Lainesse 1.0
Carlos Hernandez 1.0
Trey Ogden 1.0
Modestas Bukauskas 1.0
Hakeem Dawodu 1.0
JP Buys 1.0
Aleksa Camur 1.0
Journey Newson 1.0
Top ten fighters for FA_SIG_%:
FIGHTER FA_SIG_%
Danilo Marques 0.832335
Alistair Overeem 0.738928
Cyrille Diabate 0.717838
Tatsuro Taira 0.710297
Darrick Minner 0.707265
Umar Nurmagomedov 0.696228
Tom Aspinall 0.675439
Brock Lesnar 0.673729
Jordan Wright 0.667735
Francisco Figueiredo 0.664557
Top ten fighters for FA_STR_%:
FIGHTER FA_STR_%
Tony DeSouza 0.794118
Darrick Minner 0.792929
Brock Lesnar 0.791922
Danilo Marques 0.790503
Aaron Phillips 0.787684
Jordan Wright 0.782555
Eugene Jackson 0.778626
Alistair Overeem 0.770993
Nate Maness 0.770844
Pat Sabatini 0.765889
Top ten fighters for FA_TD_DEF_%:
FIGHTER FA_TD_DEF_%
Jailton Almeida 1.0
Philipe Lins 1.0
Umar Nurmagomedov 1.0
Yazmin Jauregui 1.0
Gregory Rodrigues 1.0
Rodolfo Vieira 1.0
Zac Pauga 1.0
Martin Buday 1.0
Shavkat Rakhmonov 1.0
Trey Ogden 1.0
Top ten fighters for FA_HEAD_DEF_%:
FIGHTER FA_HEAD_DEF_%
Jon Madsen 0.889740
Adriano Martins 0.851585
Umar Nurmagomedov 0.848684
Miguel Torres 0.829341
Claude Patrick 0.821256
Manon Fiorot 0.819853
Demetrious Johnson 0.818847
Tom Aspinall 0.815217
Desmond Green 0.814208
Montel Jackson 0.812734
Top ten fighters for FA_BODY_DEF_%:
FIGHTER FA_BODY_DEF_%
Jason Witt 1.000000
Hunter Azure 0.650407
Ivan Salaverry 0.636364
Frank Trigg 0.612903
Viscardi Andrade 0.611111
Michael Chandler 0.578125
Kajan Johnson 0.566667
Desmond Green 0.564286
Ryan Janes 0.557692
Myles Jury 0.548936
Top ten fighters for FA_LEG_DEF_%:
FIGHTER FA_LEG_DEF_%
Charles Byrd 0.666667
Josh Samman 0.666667
Mike Malott 0.650000
Vaughan Lee 0.647059
Muhammad Mokaev 0.611111
Jason Witt 0.600000
Anthony Birchak 0.595238
Jordan Leavitt 0.565217
Che Mills 0.560976
Kyle Bradley 0.545455
Top ten fighters for FA_DISTANCE_DEF_%:
FIGHTER FA_DISTANCE_DEF_%
Jon Madsen 0.847605
Marcio Cruz 0.828125
Ivan Salaverry 0.826316
Claude Patrick 0.819820
Rory Singer 0.802667
Miguel Torres 0.791209
Kenny Florian 0.786079
Che Mills 0.782609
Luke Cummo 0.781955
Terry Etim 0.774265
Top ten fighters for FA_CLINCH_DEF_%:
FIGHTER FA_CLINCH_DEF_%
Trey Ogden 1.000000
Kazula Vargas 0.857143
Adrian Yanez 0.769231
Zhang Tiequan 0.750000
Tony Ferguson 0.710956
Curtis Millender 0.666667
Robert Whiteford 0.666667
Ryan Jensen 0.666667
Jason Gonzalez 0.653846
Al Iaquinta 0.652174
Top ten fighters for FA_GROUND_DEF_%:
FIGHTER FA_GROUND_DEF_%
Daniel Zellhuber 1.000000
Michael Morales 1.000000
Trey Ogden 1.000000
Tom Aspinall 1.000000
Rashid Magomedov 1.000000
Kennedy Nzechukwu 0.771930
Johnny Bedford 0.750000
Fredy Serrano 0.714286
Darren Uyenoyama 0.710280
Calvin Kattar 0.705882
Top ten fighters for FA_SIG_DEF_%:
FIGHTER FA_SIG_DEF_%
Jon Madsen 0.804749
Miguel Torres 0.747525
Phil Davis 0.741087
Nate Mohr 0.728291
Umar Nurmagomedov 0.726190
Danielle Taylor 0.716895
Miles Johns 0.716372
Mara Romero Borella 0.707038
Manon Fiorot 0.705787
Georges St-Pierre 0.705286
Top ten fighters for FA_STR_DEF_%:
FIGHTER FA_STR_DEF_%
Jon Madsen 0.734503
Danielle Taylor 0.700000
Phil Davis 0.695577
Aiemann Zahabi 0.690476
Miles Johns 0.683609
Adriano Martins 0.679681
Jack Della Maddalena 0.668246
Miguel Torres 0.664596
Manon Fiorot 0.661639
Nate Mohr 0.660000
fa_champions = fa[fa['EITHER_CHAMP'] == 'Yes']
fa_champions
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | FA_FIGHTER | FA_KD | FA_TD | FA_SUB.ATT | FA_REV | FA_CTRL | FA_HEAD | FA_BODY | FA_LEG | FA_DISTANCE | FA_CLINCH | FA_GROUND | FA_SIG | FA_TOT.SIG | FA_STR | FA_TOTAL.STR | FA_TOTAL.TD | FA_TOTAL.HEAD | FA_TOTAL.BODY | FA_TOTAL.LEG | FA_TOTAL.DISTANCE | FA_TOTAL.CLINCH | FA_TOTAL.GROUND | FA_OPP_KD | FA_OPP_TD | FA_OPP_SUB.ATT | FA_OPP_REV | FA_OPP_CTRL | FA_OPP_HEAD | FA_OPP_BODY | FA_OPP_LEG | FA_OPP_DISTANCE | FA_OPP_CLINCH | FA_OPP_GROUND | FA_OPP_SIG | FA_OPP_TOT.SIG | FA_OPP_STR | FA_OPP_TOTAL.STR | FA_OPP_TOTAL.TD | FA_OPP_TOTAL.HEAD | FA_OPP_TOTAL.BODY | FA_OPP_TOTAL.LEG | FA_OPP_TOTAL.DISTANCE | FA_OPP_TOTAL.CLINCH | FA_OPP_TOTAL.GROUND | FA_TD_DEF | FA_HEAD_DEF | FA_BODY_DEF | FA_LEG_DEF | FA_DISTANCE_DEF | FA_CLINCH_DEF | FA_GROUND_DEF | FA_SIG_DEF | FA_STR_DEF | FA_TD_% | FA_HEAD_% | FA_BODY_% | FA_LEG_% | FA_DISTANCE_% | FA_CLINCH_% | FA_GROUND_% | FA_SIG_% | FA_STR_% | FA_TD_DEF_% | FA_HEAD_DEF_% | FA_BODY_DEF_% | FA_LEG_DEF_% | FA_DISTANCE_DEF_% | FA_CLINCH_DEF_% | FA_GROUND_DEF_% | FA_SIG_DEF_% | FA_STR_DEF_% | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5 | Dustin Poirier | Southpaw | No | Yes | Yes | Yes | Dustin Poirier | 0.333333 | 0.380000 | 0.415556 | 0.038889 | 0.931787 | 18.404444 | 1.986667 | 2.854444 | 16.203889 | 4.217222 | 2.824444 | 23.245556 | 45.263333 | 27.461667 | 50.016111 | 0.986111 | 38.940556 | 2.903333 | 3.419444 | 34.181111 | 6.597778 | 4.484444 | 0.083333 | 0.337778 | 0.337222 | 0.033333 | 0.737278 | 11.157222 | 3.819444 | 2.393889 | 12.997778 | 2.606667 | 1.766111 | 17.370556 | 38.315556 | 22.536111 | 44.076111 | 1.026667 | 29.803889 | 5.589444 | 2.922222 | 32.151667 | 3.980000 | 2.183889 | 0.688889 | 18.646667 | 1.770000 | 0.528333 | 19.153889 | 1.373333 | 0.417778 | 20.945000 | 21.540000 | 0.385352 | 0.472629 | 0.684271 | 0.834768 | 0.474060 | 0.639188 | 0.629832 | 0.513563 | 0.549056 | 0.670996 | 0.625645 | 0.316668 | 0.180798 | 0.595735 | 0.345059 | 0.191300 | 0.546645 | 0.488700 |
| 17 | Rafael Dos Anjos | Southpaw | Yes | No | Yes | Yes | Rafael Dos Anjos | 0.124762 | 0.565714 | 0.243333 | 0.022381 | 1.195627 | 9.042857 | 3.488095 | 2.694762 | 9.950000 | 2.444762 | 2.830952 | 15.225714 | 32.731905 | 21.592857 | 39.883810 | 1.515714 | 24.749048 | 4.707143 | 3.275714 | 25.424762 | 3.172381 | 4.134762 | 0.015238 | 0.605714 | 0.023810 | 0.019048 | 0.969476 | 8.720476 | 2.871429 | 1.960000 | 10.389048 | 2.190000 | 0.972857 | 13.551905 | 35.189048 | 19.921905 | 42.335238 | 1.410476 | 28.632857 | 4.305714 | 2.250476 | 30.766667 | 3.007619 | 1.414762 | 0.804762 | 19.912381 | 1.434286 | 0.290476 | 20.377619 | 0.817619 | 0.441905 | 21.637143 | 22.413333 | 0.373233 | 0.365382 | 0.741022 | 0.822649 | 0.391351 | 0.770639 | 0.684671 | 0.465164 | 0.541394 | 0.570560 | 0.695438 | 0.333112 | 0.129073 | 0.662328 | 0.271849 | 0.312353 | 0.614883 | 0.529425 |
| 22 | Petr Yan | Switch | Yes | Yes | Yes | Yes | Petr Yan | 0.332051 | 0.614103 | 0.053846 | 0.041026 | 0.725256 | 17.366667 | 5.467949 | 2.526923 | 18.220513 | 2.866667 | 4.274359 | 25.361538 | 47.634615 | 32.408974 | 55.421795 | 1.170513 | 38.164103 | 6.756410 | 2.714103 | 38.679487 | 3.556410 | 5.398718 | 0.025641 | 0.337179 | 0.000000 | 0.034615 | 0.479979 | 12.464103 | 4.293590 | 3.071795 | 17.519231 | 1.701282 | 0.608974 | 19.829487 | 49.025641 | 22.821795 | 52.474359 | 2.488462 | 37.915385 | 6.943590 | 4.166667 | 45.834615 | 2.366667 | 0.824359 | 2.151282 | 25.451282 | 2.650000 | 1.094872 | 28.315385 | 0.665385 | 0.215385 | 29.196154 | 29.652564 | 0.524644 | 0.455052 | 0.809298 | 0.931034 | 0.471064 | 0.806056 | 0.791736 | 0.532418 | 0.584769 | 0.864503 | 0.671265 | 0.381647 | 0.262769 | 0.617773 | 0.281148 | 0.261275 | 0.595528 | 0.565087 |
| 27 | Sean O'Malley | Switch | Yes | No | Yes | Yes | Sean O'Malley | 0.291667 | 0.083333 | 0.083333 | 0.027778 | 0.061944 | 19.833333 | 6.572222 | 2.955556 | 27.297222 | 0.508333 | 1.555556 | 29.361111 | 47.350000 | 30.158333 | 48.313889 | 0.194444 | 35.702778 | 8.536111 | 3.111111 | 44.880556 | 0.608333 | 1.861111 | 0.000000 | 0.305556 | 0.000000 | 0.000000 | 0.411574 | 5.475000 | 2.386111 | 5.552778 | 11.944444 | 0.441667 | 1.027778 | 13.413889 | 35.461111 | 15.483333 | 37.863889 | 0.916667 | 23.291667 | 5.111111 | 7.058333 | 33.277778 | 0.877778 | 1.305556 | 0.611111 | 17.816667 | 2.725000 | 1.505556 | 21.333333 | 0.436111 | 0.277778 | 22.047222 | 22.380556 | 0.428571 | 0.555512 | 0.769932 | 0.950000 | 0.608219 | 0.835616 | 0.835821 | 0.620087 | 0.624217 | 0.666667 | 0.764937 | 0.533152 | 0.213302 | 0.641068 | 0.496835 | 0.212766 | 0.621730 | 0.591079 |
| 50 | Brandon Moreno | Orthodox | Yes | Yes | Yes | Yes | Brandon Moreno | 0.093750 | 0.633333 | 0.354167 | 0.110417 | 1.154444 | 12.929167 | 2.818750 | 1.806250 | 14.433333 | 1.043750 | 2.077083 | 17.554167 | 40.518750 | 25.658333 | 49.141667 | 1.233333 | 34.529167 | 3.843750 | 2.145833 | 35.479167 | 1.837500 | 3.202083 | 0.070833 | 0.491667 | 0.062500 | 0.072917 | 0.575937 | 10.168750 | 2.983333 | 2.781250 | 13.529167 | 0.658333 | 1.745833 | 15.933333 | 39.929167 | 20.997917 | 45.800000 | 1.279167 | 31.747917 | 4.414583 | 3.766667 | 36.704167 | 1.162500 | 2.062500 | 0.787500 | 21.579167 | 1.431250 | 0.985417 | 23.175000 | 0.504167 | 0.316667 | 23.995833 | 24.802083 | 0.513514 | 0.374442 | 0.733333 | 0.841748 | 0.406812 | 0.568027 | 0.648666 | 0.433236 | 0.522130 | 0.615635 | 0.679703 | 0.324210 | 0.261615 | 0.631400 | 0.433692 | 0.153535 | 0.600960 | 0.541530 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2165 | Dave Menne | Orthodox | Yes | No | Yes | Yes | Dave Menne | 0.000000 | 0.106667 | 0.893333 | 0.146667 | 0.350000 | 2.486667 | 1.606667 | 2.773333 | 4.126667 | 2.033333 | 0.706667 | 6.866667 | 20.366667 | 12.700000 | 26.440000 | 0.506667 | 13.973333 | 1.753333 | 4.640000 | 16.393333 | 2.880000 | 1.093333 | 0.500000 | 0.946667 | 0.066667 | 0.066667 | 1.639000 | 7.333333 | 2.286667 | 0.840000 | 5.480000 | 1.580000 | 3.400000 | 10.460000 | 19.333333 | 19.933333 | 29.606667 | 2.126667 | 15.833333 | 2.460000 | 1.040000 | 12.220000 | 2.446667 | 4.666667 | 1.180000 | 8.500000 | 0.173333 | 0.200000 | 6.740000 | 0.866667 | 1.266667 | 8.873333 | 9.673333 | 0.210526 | 0.177958 | 0.916350 | 0.597701 | 0.251728 | 0.706019 | 0.646341 | 0.337152 | 0.480333 | 0.554859 | 0.536842 | 0.070461 | 0.192308 | 0.551555 | 0.354223 | 0.271429 | 0.458966 | 0.326728 |
| 2241 | Carlos Newton | Orthodox | Yes | No | Yes | Yes | Carlos Newton | 0.000000 | 0.600000 | 0.416667 | 0.250000 | 0.993889 | 0.683333 | 0.666667 | 0.200000 | 0.716667 | 0.666667 | 0.166667 | 1.550000 | 5.216667 | 7.000000 | 11.066667 | 0.983333 | 4.283333 | 0.733333 | 0.200000 | 3.700000 | 1.016667 | 0.500000 | 0.000000 | 0.800000 | 0.216667 | 0.066667 | 1.906389 | 5.450000 | 1.050000 | 0.733333 | 2.200000 | 0.583333 | 4.450000 | 7.233333 | 13.116667 | 18.800000 | 25.683333 | 0.966667 | 11.283333 | 1.100000 | 0.733333 | 6.083333 | 0.783333 | 6.250000 | 0.166667 | 5.833333 | 0.050000 | 0.000000 | 3.883333 | 0.200000 | 1.800000 | 5.883333 | 6.883333 | 0.610169 | 0.159533 | 0.909091 | 1.000000 | 0.193694 | 0.655738 | 0.333333 | 0.297125 | 0.632530 | 0.172414 | 0.516987 | 0.045455 | 0.000000 | 0.638356 | 0.255319 | 0.288000 | 0.448539 | 0.268008 |
| 2246 | Ricco Rodriguez | Orthodox | Yes | No | Yes | Yes | Ricco Rodriguez | 0.000000 | 0.923810 | 0.409524 | 0.119048 | 2.018254 | 7.495238 | 1.280952 | 0.695238 | 2.509524 | 1.342857 | 5.619048 | 9.471429 | 16.190476 | 24.466667 | 32.152381 | 2.357143 | 13.747619 | 1.700000 | 0.742857 | 5.909524 | 1.685714 | 8.595238 | 0.142857 | 0.309524 | 0.071429 | 0.028571 | 1.013095 | 3.628571 | 0.714286 | 1.485714 | 2.352381 | 0.785714 | 2.690476 | 5.828571 | 13.633333 | 12.071429 | 20.209524 | 0.547619 | 11.071429 | 0.790476 | 1.771429 | 7.766667 | 1.490476 | 4.376190 | 0.238095 | 7.442857 | 0.076190 | 0.285714 | 5.414286 | 0.704762 | 1.685714 | 7.804762 | 8.138095 | 0.391919 | 0.545203 | 0.753501 | 0.935897 | 0.424658 | 0.796610 | 0.653740 | 0.585000 | 0.760960 | 0.434783 | 0.672258 | 0.096386 | 0.161290 | 0.697118 | 0.472843 | 0.385201 | 0.572476 | 0.402686 |
| 2278 | Pat Miletich | Orthodox | Yes | No | Yes | Yes | Pat Miletich | 0.100000 | 0.600000 | 0.300000 | 0.000000 | 1.392222 | 4.166667 | 0.566667 | 0.833333 | 3.300000 | 0.800000 | 1.466667 | 5.566667 | 11.666667 | 18.633333 | 24.966667 | 0.600000 | 9.966667 | 0.666667 | 1.033333 | 8.633333 | 1.200000 | 1.833333 | 0.000000 | 0.466667 | 0.266667 | 0.000000 | 0.869444 | 4.366667 | 1.366667 | 1.000000 | 1.800000 | 0.866667 | 4.066667 | 6.733333 | 16.566667 | 18.933333 | 29.800000 | 1.000000 | 13.800000 | 1.566667 | 1.200000 | 7.466667 | 1.100000 | 8.000000 | 0.533333 | 9.433333 | 0.200000 | 0.200000 | 5.666667 | 0.233333 | 3.933333 | 9.833333 | 10.866667 | 1.000000 | 0.418060 | 0.850000 | 0.806452 | 0.382239 | 0.666667 | 0.800000 | 0.477143 | 0.746328 | 0.533333 | 0.683575 | 0.127660 | 0.166667 | 0.758929 | 0.212121 | 0.491667 | 0.593561 | 0.364653 |
| 2282 | Kevin Randleman | Orthodox | Yes | No | Yes | Yes | Kevin Randleman | 0.000000 | 0.350000 | 0.183333 | 0.000000 | 2.147778 | 3.016667 | 0.500000 | 0.083333 | 0.800000 | 0.600000 | 2.200000 | 3.600000 | 7.433333 | 11.816667 | 16.333333 | 0.950000 | 6.850000 | 0.500000 | 0.083333 | 2.533333 | 0.700000 | 4.200000 | 0.250000 | 0.083333 | 0.500000 | 0.000000 | 0.254167 | 2.150000 | 0.916667 | 0.783333 | 1.100000 | 1.500000 | 1.250000 | 3.850000 | 7.766667 | 9.716667 | 13.900000 | 0.083333 | 5.883333 | 1.000000 | 0.883333 | 4.250000 | 1.716667 | 1.800000 | 0.000000 | 3.733333 | 0.083333 | 0.100000 | 3.150000 | 0.216667 | 0.550000 | 3.916667 | 4.183333 | 0.368421 | 0.440389 | 1.000000 | 1.000000 | 0.315789 | 0.857143 | 0.523810 | 0.484305 | 0.723469 | 0.000000 | 0.634561 | 0.083333 | 0.113208 | 0.741176 | 0.126214 | 0.305556 | 0.504292 | 0.300959 |
103 rows × 78 columns
# Calculate the mean for each of the specified columns
means1 = fa_champions[metrics1].mean()
# Rank the means from highest to lowest and plot again
sorted_means1 = means1.sort_values(ascending=False)
# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_means1.plot(kind='bar', color='dodgerblue')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90) # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()
# Display max rows
pd.set_option('display.max_rows', None)
print(sorted_means1)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)
RA_STR 25.885904 RA_SIG 17.712756 RA_DISTANCE 12.677297 RA_HEAD 11.715603 RA_BODY 3.407324 RA_GROUND 2.689004 RA_LEG 2.589829 RA_CLINCH 2.346455 RA_CTRL 1.002583 RA_TD 0.468739 RA_SUB.ATT 0.153378 RA_KD 0.092750 RA_REV 0.045344 dtype: float64
# Calculate the mean for each of the specified columns
meansA = fa_champions[metrics2].mean()
# Rank the means from highest to lowest and plot again
sorted_meansA = meansA.sort_values(ascending=False)
# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_meansA.plot(kind='bar', color='fuchsia')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)
plt.show()
# Display max rows
pd.set_option('display.max_rows', None)
print(sorted_meansA)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)
RA_LEG_% 0.850586 RA_BODY_% 0.747112 RA_CLINCH_% 0.720817 RA_GROUND_% 0.697236 RA_TD_DEF_% 0.653601 RA_HEAD_DEF_% 0.643140 RA_DISTANCE_DEF_% 0.615557 RA_STR_% 0.571944 RA_SIG_DEF_% 0.561156 RA_SIG_% 0.484442 RA_STR_DEF_% 0.475463 RA_TD_% 0.472020 RA_DISTANCE_% 0.415333 RA_HEAD_% 0.406465 RA_GROUND_DEF_% 0.311422 RA_CLINCH_DEF_% 0.306687 RA_BODY_DEF_% 0.296030 RA_LEG_DEF_% 0.194439 dtype: float64
fa_challengers = fa[fa['EVER_CHALLENGER'] == 'Yes']
fa_challengers
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | FA_FIGHTER | FA_KD | FA_TD | FA_SUB.ATT | FA_REV | FA_CTRL | FA_HEAD | FA_BODY | FA_LEG | FA_DISTANCE | FA_CLINCH | FA_GROUND | FA_SIG | FA_TOT.SIG | FA_STR | FA_TOTAL.STR | FA_TOTAL.TD | FA_TOTAL.HEAD | FA_TOTAL.BODY | FA_TOTAL.LEG | FA_TOTAL.DISTANCE | FA_TOTAL.CLINCH | FA_TOTAL.GROUND | FA_OPP_KD | FA_OPP_TD | FA_OPP_SUB.ATT | FA_OPP_REV | FA_OPP_CTRL | FA_OPP_HEAD | FA_OPP_BODY | FA_OPP_LEG | FA_OPP_DISTANCE | FA_OPP_CLINCH | FA_OPP_GROUND | FA_OPP_SIG | FA_OPP_TOT.SIG | FA_OPP_STR | FA_OPP_TOTAL.STR | FA_OPP_TOTAL.TD | FA_OPP_TOTAL.HEAD | FA_OPP_TOTAL.BODY | FA_OPP_TOTAL.LEG | FA_OPP_TOTAL.DISTANCE | FA_OPP_TOTAL.CLINCH | FA_OPP_TOTAL.GROUND | FA_TD_DEF | FA_HEAD_DEF | FA_BODY_DEF | FA_LEG_DEF | FA_DISTANCE_DEF | FA_CLINCH_DEF | FA_GROUND_DEF | FA_SIG_DEF | FA_STR_DEF | FA_TD_% | FA_HEAD_% | FA_BODY_% | FA_LEG_% | FA_DISTANCE_% | FA_CLINCH_% | FA_GROUND_% | FA_SIG_% | FA_STR_% | FA_TD_DEF_% | FA_HEAD_DEF_% | FA_BODY_DEF_% | FA_LEG_DEF_% | FA_DISTANCE_DEF_% | FA_CLINCH_DEF_% | FA_GROUND_DEF_% | FA_SIG_DEF_% | FA_STR_DEF_% | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5 | Dustin Poirier | Southpaw | No | Yes | Yes | Yes | Dustin Poirier | 0.333333 | 0.380000 | 0.415556 | 0.038889 | 0.931787 | 18.404444 | 1.986667 | 2.854444 | 16.203889 | 4.217222 | 2.824444 | 23.245556 | 45.263333 | 27.461667 | 50.016111 | 0.986111 | 38.940556 | 2.903333 | 3.419444 | 34.181111 | 6.597778 | 4.484444 | 0.083333 | 0.337778 | 0.337222 | 0.033333 | 0.737278 | 11.157222 | 3.819444 | 2.393889 | 12.997778 | 2.606667 | 1.766111 | 17.370556 | 38.315556 | 22.536111 | 44.076111 | 1.026667 | 29.803889 | 5.589444 | 2.922222 | 32.151667 | 3.980000 | 2.183889 | 0.688889 | 18.646667 | 1.770000 | 0.528333 | 19.153889 | 1.373333 | 0.417778 | 20.945000 | 21.540000 | 0.385352 | 0.472629 | 0.684271 | 0.834768 | 0.474060 | 0.639188 | 0.629832 | 0.513563 | 0.549056 | 0.670996 | 0.625645 | 0.316668 | 0.180798 | 0.595735 | 0.345059 | 0.191300 | 0.546645 | 0.488700 |
| 6 | Gilbert Burns | Orthodox | No | No | Yes | No | Gilbert Burns | 0.106061 | 0.737879 | 0.259091 | 0.000000 | 1.261818 | 9.092424 | 3.015152 | 2.731818 | 10.783333 | 1.413636 | 2.642424 | 14.839394 | 30.507576 | 21.321212 | 37.996970 | 1.813636 | 23.080303 | 3.980303 | 3.446970 | 25.089394 | 2.122727 | 3.295455 | 0.181818 | 0.250000 | 0.045455 | 0.000000 | 0.455985 | 9.357576 | 2.880303 | 2.640909 | 12.509091 | 1.180303 | 1.189394 | 14.878788 | 32.503030 | 19.245455 | 37.415152 | 0.457576 | 24.954545 | 4.557576 | 2.990909 | 29.171212 | 1.672727 | 1.659091 | 0.207576 | 15.596970 | 1.677273 | 0.350000 | 16.662121 | 0.492424 | 0.469697 | 17.624242 | 18.169697 | 0.406850 | 0.393947 | 0.757518 | 0.792527 | 0.429796 | 0.665953 | 0.801839 | 0.486417 | 0.561129 | 0.453642 | 0.625015 | 0.368019 | 0.117021 | 0.571184 | 0.294384 | 0.283105 | 0.542234 | 0.485624 |
| 17 | Rafael Dos Anjos | Southpaw | Yes | No | Yes | Yes | Rafael Dos Anjos | 0.124762 | 0.565714 | 0.243333 | 0.022381 | 1.195627 | 9.042857 | 3.488095 | 2.694762 | 9.950000 | 2.444762 | 2.830952 | 15.225714 | 32.731905 | 21.592857 | 39.883810 | 1.515714 | 24.749048 | 4.707143 | 3.275714 | 25.424762 | 3.172381 | 4.134762 | 0.015238 | 0.605714 | 0.023810 | 0.019048 | 0.969476 | 8.720476 | 2.871429 | 1.960000 | 10.389048 | 2.190000 | 0.972857 | 13.551905 | 35.189048 | 19.921905 | 42.335238 | 1.410476 | 28.632857 | 4.305714 | 2.250476 | 30.766667 | 3.007619 | 1.414762 | 0.804762 | 19.912381 | 1.434286 | 0.290476 | 20.377619 | 0.817619 | 0.441905 | 21.637143 | 22.413333 | 0.373233 | 0.365382 | 0.741022 | 0.822649 | 0.391351 | 0.770639 | 0.684671 | 0.465164 | 0.541394 | 0.570560 | 0.695438 | 0.333112 | 0.129073 | 0.662328 | 0.271849 | 0.312353 | 0.614883 | 0.529425 |
| 22 | Petr Yan | Switch | Yes | Yes | Yes | Yes | Petr Yan | 0.332051 | 0.614103 | 0.053846 | 0.041026 | 0.725256 | 17.366667 | 5.467949 | 2.526923 | 18.220513 | 2.866667 | 4.274359 | 25.361538 | 47.634615 | 32.408974 | 55.421795 | 1.170513 | 38.164103 | 6.756410 | 2.714103 | 38.679487 | 3.556410 | 5.398718 | 0.025641 | 0.337179 | 0.000000 | 0.034615 | 0.479979 | 12.464103 | 4.293590 | 3.071795 | 17.519231 | 1.701282 | 0.608974 | 19.829487 | 49.025641 | 22.821795 | 52.474359 | 2.488462 | 37.915385 | 6.943590 | 4.166667 | 45.834615 | 2.366667 | 0.824359 | 2.151282 | 25.451282 | 2.650000 | 1.094872 | 28.315385 | 0.665385 | 0.215385 | 29.196154 | 29.652564 | 0.524644 | 0.455052 | 0.809298 | 0.931034 | 0.471064 | 0.806056 | 0.791736 | 0.532418 | 0.584769 | 0.864503 | 0.671265 | 0.381647 | 0.262769 | 0.617773 | 0.281148 | 0.261275 | 0.595528 | 0.565087 |
| 26 | Marlon Vera | Switch | No | No | Yes | No | Marlon Vera | 0.189130 | 0.188406 | 0.349275 | 0.014493 | 0.471957 | 10.335507 | 4.074638 | 4.638406 | 14.327536 | 2.131159 | 2.589855 | 19.048551 | 37.742029 | 22.190580 | 41.505797 | 0.450725 | 25.532609 | 6.634058 | 5.575362 | 31.330435 | 2.681159 | 3.730435 | 0.000000 | 0.360870 | 0.000000 | 0.028986 | 0.972742 | 13.055072 | 5.043478 | 3.755072 | 17.626812 | 1.728261 | 2.498551 | 21.853623 | 44.216667 | 28.155072 | 51.447101 | 1.176087 | 32.431159 | 7.140580 | 4.644928 | 38.248551 | 2.517391 | 3.450725 | 0.815217 | 19.376087 | 2.097101 | 0.889855 | 20.621739 | 0.789130 | 0.952174 | 22.363043 | 23.292029 | 0.418006 | 0.404796 | 0.614200 | 0.831947 | 0.457304 | 0.794865 | 0.694250 | 0.504704 | 0.534638 | 0.693161 | 0.597453 | 0.293688 | 0.191576 | 0.539151 | 0.313472 | 0.275934 | 0.505761 | 0.452737 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2245 | Pedro Rizzo | Orthodox | No | No | Yes | No | Pedro Rizzo | 0.203333 | 0.083333 | 0.000000 | 0.000000 | 0.516444 | 4.200000 | 0.643333 | 4.120000 | 6.436667 | 1.273333 | 1.253333 | 8.963333 | 23.783333 | 12.573333 | 27.723333 | 0.283333 | 17.753333 | 0.643333 | 5.386667 | 19.616667 | 1.833333 | 2.333333 | 0.050000 | 0.366667 | 0.040000 | 0.000000 | 0.702611 | 6.693333 | 1.210000 | 3.100000 | 7.830000 | 1.656667 | 1.516667 | 11.003333 | 25.166667 | 18.116667 | 32.740000 | 1.980000 | 19.476667 | 1.613333 | 4.076667 | 20.470000 | 2.700000 | 1.996667 | 1.613333 | 12.783333 | 0.403333 | 0.976667 | 12.640000 | 1.043333 | 0.480000 | 14.163333 | 14.623333 | 0.294118 | 0.236575 | 1.000000 | 0.764851 | 0.328122 | 0.694545 | 0.537143 | 0.376875 | 0.453529 | 0.814815 | 0.656341 | 0.250000 | 0.239575 | 0.617489 | 0.386420 | 0.240401 | 0.562781 | 0.446650 |
| 2246 | Ricco Rodriguez | Orthodox | Yes | No | Yes | Yes | Ricco Rodriguez | 0.000000 | 0.923810 | 0.409524 | 0.119048 | 2.018254 | 7.495238 | 1.280952 | 0.695238 | 2.509524 | 1.342857 | 5.619048 | 9.471429 | 16.190476 | 24.466667 | 32.152381 | 2.357143 | 13.747619 | 1.700000 | 0.742857 | 5.909524 | 1.685714 | 8.595238 | 0.142857 | 0.309524 | 0.071429 | 0.028571 | 1.013095 | 3.628571 | 0.714286 | 1.485714 | 2.352381 | 0.785714 | 2.690476 | 5.828571 | 13.633333 | 12.071429 | 20.209524 | 0.547619 | 11.071429 | 0.790476 | 1.771429 | 7.766667 | 1.490476 | 4.376190 | 0.238095 | 7.442857 | 0.076190 | 0.285714 | 5.414286 | 0.704762 | 1.685714 | 7.804762 | 8.138095 | 0.391919 | 0.545203 | 0.753501 | 0.935897 | 0.424658 | 0.796610 | 0.653740 | 0.585000 | 0.760960 | 0.434783 | 0.672258 | 0.096386 | 0.161290 | 0.697118 | 0.472843 | 0.385201 | 0.572476 | 0.402686 |
| 2251 | Gan McGee | Orthodox | No | No | Yes | No | Gan McGee | 0.000000 | 0.250000 | 0.000000 | 0.000000 | 1.312500 | 7.875000 | 1.000000 | 0.625000 | 6.375000 | 0.250000 | 2.875000 | 9.500000 | 29.125000 | 12.250000 | 32.125000 | 1.375000 | 27.250000 | 1.000000 | 0.875000 | 23.250000 | 0.500000 | 5.375000 | 0.250000 | 0.125000 | 0.375000 | 0.000000 | 0.662500 | 4.750000 | 0.625000 | 3.000000 | 5.125000 | 1.000000 | 2.250000 | 8.375000 | 19.500000 | 11.750000 | 23.375000 | 1.875000 | 15.875000 | 0.625000 | 3.000000 | 15.500000 | 1.125000 | 2.875000 | 1.750000 | 11.125000 | 0.000000 | 0.000000 | 10.375000 | 0.125000 | 0.625000 | 11.125000 | 11.625000 | 0.181818 | 0.288991 | 1.000000 | 0.714286 | 0.274194 | 0.500000 | 0.534884 | 0.326180 | 0.381323 | 0.933333 | 0.700787 | 0.000000 | 0.000000 | 0.669355 | 0.111111 | 0.217391 | 0.570513 | 0.497326 |
| 2278 | Pat Miletich | Orthodox | Yes | No | Yes | Yes | Pat Miletich | 0.100000 | 0.600000 | 0.300000 | 0.000000 | 1.392222 | 4.166667 | 0.566667 | 0.833333 | 3.300000 | 0.800000 | 1.466667 | 5.566667 | 11.666667 | 18.633333 | 24.966667 | 0.600000 | 9.966667 | 0.666667 | 1.033333 | 8.633333 | 1.200000 | 1.833333 | 0.000000 | 0.466667 | 0.266667 | 0.000000 | 0.869444 | 4.366667 | 1.366667 | 1.000000 | 1.800000 | 0.866667 | 4.066667 | 6.733333 | 16.566667 | 18.933333 | 29.800000 | 1.000000 | 13.800000 | 1.566667 | 1.200000 | 7.466667 | 1.100000 | 8.000000 | 0.533333 | 9.433333 | 0.200000 | 0.200000 | 5.666667 | 0.233333 | 3.933333 | 9.833333 | 10.866667 | 1.000000 | 0.418060 | 0.850000 | 0.806452 | 0.382239 | 0.666667 | 0.800000 | 0.477143 | 0.746328 | 0.533333 | 0.683575 | 0.127660 | 0.166667 | 0.758929 | 0.212121 | 0.491667 | 0.593561 | 0.364653 |
| 2282 | Kevin Randleman | Orthodox | Yes | No | Yes | Yes | Kevin Randleman | 0.000000 | 0.350000 | 0.183333 | 0.000000 | 2.147778 | 3.016667 | 0.500000 | 0.083333 | 0.800000 | 0.600000 | 2.200000 | 3.600000 | 7.433333 | 11.816667 | 16.333333 | 0.950000 | 6.850000 | 0.500000 | 0.083333 | 2.533333 | 0.700000 | 4.200000 | 0.250000 | 0.083333 | 0.500000 | 0.000000 | 0.254167 | 2.150000 | 0.916667 | 0.783333 | 1.100000 | 1.500000 | 1.250000 | 3.850000 | 7.766667 | 9.716667 | 13.900000 | 0.083333 | 5.883333 | 1.000000 | 0.883333 | 4.250000 | 1.716667 | 1.800000 | 0.000000 | 3.733333 | 0.083333 | 0.100000 | 3.150000 | 0.216667 | 0.550000 | 3.916667 | 4.183333 | 0.368421 | 0.440389 | 1.000000 | 1.000000 | 0.315789 | 0.857143 | 0.523810 | 0.484305 | 0.723469 | 0.000000 | 0.634561 | 0.083333 | 0.113208 | 0.741176 | 0.126214 | 0.305556 | 0.504292 | 0.300959 |
213 rows × 78 columns
# Calculate the mean for each of the specified columns
means2 = fa_challengers[metrics1].mean()
# Rank the means from highest to lowest and plot again
sorted_means2 = means2.sort_values(ascending=False)
# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_means2.plot(kind='bar', color='darkcyan')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90) # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()
# Display max rows
pd.set_option('display.max_rows', None)
print(sorted_means2)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)
RA_STR 23.677031 RA_SIG 15.668471 RA_DISTANCE 11.296105 RA_HEAD 10.307098 RA_BODY 3.061733 RA_GROUND 2.332257 RA_LEG 2.299641 RA_CLINCH 2.040109 RA_CTRL 0.935548 RA_TD 0.407687 RA_SUB.ATT 0.147046 RA_KD 0.088746 RA_REV 0.046639 dtype: float64
# Calculate the mean for each of the specified columns
meansB = fa_challengers[metrics2].mean()
# Rank the means from highest to lowest and plot again
sorted_meansB = meansB.sort_values(ascending=False)
# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_meansB.plot(kind='bar', color='peru')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)
plt.show()
# Display max rows
pd.set_option('display.max_rows', None)
print(sorted_meansB)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)
RA_LEG_% 0.820616 RA_BODY_% 0.720454 RA_CLINCH_% 0.702440 RA_GROUND_% 0.688784 RA_HEAD_DEF_% 0.630248 RA_TD_DEF_% 0.613314 RA_DISTANCE_DEF_% 0.608496 RA_STR_% 0.547432 RA_SIG_DEF_% 0.547316 RA_STR_DEF_% 0.456533 RA_SIG_% 0.453429 RA_TD_% 0.402681 RA_DISTANCE_% 0.386544 RA_HEAD_% 0.375957 RA_CLINCH_DEF_% 0.309879 RA_GROUND_DEF_% 0.295873 RA_BODY_DEF_% 0.287543 RA_LEG_DEF_% 0.185764 dtype: float64
fa_non_champions = fa[fa['EITHER_CHAMP'] != 'Yes']
fa_non_champions
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | FA_FIGHTER | FA_KD | FA_TD | FA_SUB.ATT | FA_REV | FA_CTRL | FA_HEAD | FA_BODY | FA_LEG | FA_DISTANCE | FA_CLINCH | FA_GROUND | FA_SIG | FA_TOT.SIG | FA_STR | FA_TOTAL.STR | FA_TOTAL.TD | FA_TOTAL.HEAD | FA_TOTAL.BODY | FA_TOTAL.LEG | FA_TOTAL.DISTANCE | FA_TOTAL.CLINCH | FA_TOTAL.GROUND | FA_OPP_KD | FA_OPP_TD | FA_OPP_SUB.ATT | FA_OPP_REV | FA_OPP_CTRL | FA_OPP_HEAD | FA_OPP_BODY | FA_OPP_LEG | FA_OPP_DISTANCE | FA_OPP_CLINCH | FA_OPP_GROUND | FA_OPP_SIG | FA_OPP_TOT.SIG | FA_OPP_STR | FA_OPP_TOTAL.STR | FA_OPP_TOTAL.TD | FA_OPP_TOTAL.HEAD | FA_OPP_TOTAL.BODY | FA_OPP_TOTAL.LEG | FA_OPP_TOTAL.DISTANCE | FA_OPP_TOTAL.CLINCH | FA_OPP_TOTAL.GROUND | FA_TD_DEF | FA_HEAD_DEF | FA_BODY_DEF | FA_LEG_DEF | FA_DISTANCE_DEF | FA_CLINCH_DEF | FA_GROUND_DEF | FA_SIG_DEF | FA_STR_DEF | FA_TD_% | FA_HEAD_% | FA_BODY_% | FA_LEG_% | FA_DISTANCE_% | FA_CLINCH_% | FA_GROUND_% | FA_SIG_% | FA_STR_% | FA_TD_DEF_% | FA_HEAD_DEF_% | FA_BODY_DEF_% | FA_LEG_DEF_% | FA_DISTANCE_DEF_% | FA_CLINCH_DEF_% | FA_GROUND_DEF_% | FA_SIG_DEF_% | FA_STR_DEF_% | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | CJ Vergara | Orthodox | No | No | No | No | CJ Vergara | 0.000000 | 0.000000 | 0.083333 | 0.083333 | 0.694444 | 13.222222 | 8.861111 | 3.194444 | 17.111111 | 2.027778 | 6.138889 | 25.277778 | 43.722222 | 35.694444 | 55.388889 | 0.000000 | 28.388889 | 11.694444 | 3.638889 | 31.944444 | 2.694444 | 9.083333 | 0.166667 | 1.000000 | 0.388889 | 0.111111 | 1.316204 | 13.611111 | 5.500000 | 2.944444 | 19.027778 | 1.527778 | 1.500000 | 22.055556 | 46.583333 | 26.611111 | 52.277778 | 2.500000 | 36.055556 | 7.333333 | 3.194444 | 42.500000 | 2.083333 | 2.000000 | 1.500000 | 22.444444 | 1.833333 | 0.250000 | 23.472222 | 0.555556 | 0.500000 | 24.527778 | 25.666667 | NaN | 0.465753 | 0.757720 | 0.877863 | 0.535652 | 0.752577 | 0.675841 | 0.578145 | 0.644433 | 0.600000 | 0.622496 | 0.250000 | 0.078261 | 0.552288 | 0.266667 | 0.250000 | 0.526535 | 0.490967 |
| 2 | Curtis Blaydes | Orthodox | No | No | No | No | Curtis Blaydes | 0.055556 | 1.266667 | 0.000000 | 0.000000 | 1.519198 | 9.433333 | 1.475926 | 2.385185 | 6.462963 | 1.612963 | 5.218519 | 13.294444 | 27.970370 | 22.492593 | 39.153704 | 2.444444 | 23.518519 | 1.692593 | 2.759259 | 19.392593 | 2.029630 | 6.548148 | 0.157407 | 0.344444 | 0.037037 | 0.000000 | 0.201883 | 6.401852 | 1.224074 | 0.727778 | 6.603704 | 1.101852 | 0.648148 | 8.353704 | 19.874074 | 12.364815 | 24.161111 | 0.485185 | 17.364815 | 1.670370 | 0.838889 | 17.751852 | 1.344444 | 0.777778 | 0.140741 | 10.962963 | 0.446296 | 0.111111 | 11.148148 | 0.242593 | 0.129630 | 11.520370 | 11.796296 | 0.518182 | 0.401102 | 0.871991 | 0.864430 | 0.333270 | 0.794708 | 0.796946 | 0.475305 | 0.574469 | 0.290076 | 0.631332 | 0.267184 | 0.132450 | 0.627999 | 0.180441 | 0.166667 | 0.579668 | 0.488235 |
| 3 | Jailton Almeida | Orthodox | No | No | No | No | Jailton Almeida | 0.000000 | 2.028571 | 0.685714 | 0.028571 | 3.565476 | 13.557143 | 0.571429 | 0.100000 | 0.428571 | 0.085714 | 13.714286 | 14.228571 | 22.457143 | 26.142857 | 37.014286 | 3.000000 | 21.228571 | 0.857143 | 0.371429 | 1.771429 | 0.228571 | 20.457143 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.037857 | 1.700000 | 0.085714 | 0.142857 | 0.271429 | 0.028571 | 1.628571 | 1.928571 | 3.400000 | 5.800000 | 8.042857 | 0.057143 | 3.085714 | 0.171429 | 0.142857 | 1.142857 | 0.028571 | 2.228571 | 0.057143 | 1.385714 | 0.085714 | 0.000000 | 0.871429 | 0.000000 | 0.600000 | 1.471429 | 2.242857 | 0.676190 | 0.638627 | 0.666667 | 0.269231 | 0.241935 | 0.375000 | 0.670391 | 0.633588 | 0.706291 | 1.000000 | 0.449074 | 0.500000 | 0.000000 | 0.762500 | 0.000000 | 0.269231 | 0.432773 | 0.278863 |
| 4 | Benoit Saint Denis | Southpaw | No | No | No | No | Benoit Saint Denis | 0.357143 | 1.238095 | 0.500000 | 0.142857 | 1.931746 | 10.833333 | 6.380952 | 3.047619 | 11.404762 | 3.642857 | 5.214286 | 20.261905 | 37.119048 | 32.666667 | 51.357143 | 3.238095 | 25.642857 | 8.285714 | 3.190476 | 23.309524 | 4.690476 | 9.119048 | 0.071429 | 0.428571 | 0.428571 | 0.000000 | 0.255952 | 9.857143 | 4.261905 | 1.619048 | 11.976190 | 3.238095 | 0.523810 | 15.738095 | 27.547619 | 18.952381 | 31.571429 | 0.976190 | 21.023810 | 4.785714 | 1.738095 | 22.642857 | 4.380952 | 0.523810 | 0.547619 | 11.166667 | 0.523810 | 0.119048 | 10.666667 | 1.142857 | 0.000000 | 11.809524 | 12.619048 | 0.382353 | 0.422470 | 0.770115 | 0.955224 | 0.489275 | 0.776650 | 0.571802 | 0.545863 | 0.636069 | 0.560976 | 0.531144 | 0.109453 | 0.068493 | 0.471083 | 0.260870 | 0.000000 | 0.428695 | 0.399698 |
| 6 | Gilbert Burns | Orthodox | No | No | Yes | No | Gilbert Burns | 0.106061 | 0.737879 | 0.259091 | 0.000000 | 1.261818 | 9.092424 | 3.015152 | 2.731818 | 10.783333 | 1.413636 | 2.642424 | 14.839394 | 30.507576 | 21.321212 | 37.996970 | 1.813636 | 23.080303 | 3.980303 | 3.446970 | 25.089394 | 2.122727 | 3.295455 | 0.181818 | 0.250000 | 0.045455 | 0.000000 | 0.455985 | 9.357576 | 2.880303 | 2.640909 | 12.509091 | 1.180303 | 1.189394 | 14.878788 | 32.503030 | 19.245455 | 37.415152 | 0.457576 | 24.954545 | 4.557576 | 2.990909 | 29.171212 | 1.672727 | 1.659091 | 0.207576 | 15.596970 | 1.677273 | 0.350000 | 16.662121 | 0.492424 | 0.469697 | 17.624242 | 18.169697 | 0.406850 | 0.393947 | 0.757518 | 0.792527 | 0.429796 | 0.665953 | 0.801839 | 0.486417 | 0.561129 | 0.453642 | 0.625015 | 0.368019 | 0.117021 | 0.571184 | 0.294384 | 0.283105 | 0.542234 | 0.485624 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2245 | Pedro Rizzo | Orthodox | No | No | Yes | No | Pedro Rizzo | 0.203333 | 0.083333 | 0.000000 | 0.000000 | 0.516444 | 4.200000 | 0.643333 | 4.120000 | 6.436667 | 1.273333 | 1.253333 | 8.963333 | 23.783333 | 12.573333 | 27.723333 | 0.283333 | 17.753333 | 0.643333 | 5.386667 | 19.616667 | 1.833333 | 2.333333 | 0.050000 | 0.366667 | 0.040000 | 0.000000 | 0.702611 | 6.693333 | 1.210000 | 3.100000 | 7.830000 | 1.656667 | 1.516667 | 11.003333 | 25.166667 | 18.116667 | 32.740000 | 1.980000 | 19.476667 | 1.613333 | 4.076667 | 20.470000 | 2.700000 | 1.996667 | 1.613333 | 12.783333 | 0.403333 | 0.976667 | 12.640000 | 1.043333 | 0.480000 | 14.163333 | 14.623333 | 0.294118 | 0.236575 | 1.000000 | 0.764851 | 0.328122 | 0.694545 | 0.537143 | 0.376875 | 0.453529 | 0.814815 | 0.656341 | 0.250000 | 0.239575 | 0.617489 | 0.386420 | 0.240401 | 0.562781 | 0.446650 |
| 2251 | Gan McGee | Orthodox | No | No | Yes | No | Gan McGee | 0.000000 | 0.250000 | 0.000000 | 0.000000 | 1.312500 | 7.875000 | 1.000000 | 0.625000 | 6.375000 | 0.250000 | 2.875000 | 9.500000 | 29.125000 | 12.250000 | 32.125000 | 1.375000 | 27.250000 | 1.000000 | 0.875000 | 23.250000 | 0.500000 | 5.375000 | 0.250000 | 0.125000 | 0.375000 | 0.000000 | 0.662500 | 4.750000 | 0.625000 | 3.000000 | 5.125000 | 1.000000 | 2.250000 | 8.375000 | 19.500000 | 11.750000 | 23.375000 | 1.875000 | 15.875000 | 0.625000 | 3.000000 | 15.500000 | 1.125000 | 2.875000 | 1.750000 | 11.125000 | 0.000000 | 0.000000 | 10.375000 | 0.125000 | 0.625000 | 11.125000 | 11.625000 | 0.181818 | 0.288991 | 1.000000 | 0.714286 | 0.274194 | 0.500000 | 0.534884 | 0.326180 | 0.381323 | 0.933333 | 0.700787 | 0.000000 | 0.000000 | 0.669355 | 0.111111 | 0.217391 | 0.570513 | 0.497326 |
| 2252 | Ian Freeman | Orthodox | No | No | No | No | Ian Freeman | 0.000000 | 0.233333 | 0.066667 | 0.200000 | 1.591111 | 9.900000 | 1.366667 | 0.533333 | 1.300000 | 3.500000 | 7.000000 | 11.800000 | 21.266667 | 23.166667 | 33.066667 | 0.433333 | 19.100000 | 1.633333 | 0.533333 | 5.833333 | 4.633333 | 10.800000 | 0.200000 | 0.433333 | 0.600000 | 0.166667 | 1.223333 | 3.666667 | 2.700000 | 3.200000 | 3.866667 | 3.100000 | 2.600000 | 9.566667 | 16.400000 | 15.400000 | 22.700000 | 2.600000 | 9.000000 | 3.366667 | 4.033333 | 8.700000 | 3.900000 | 3.800000 | 2.166667 | 5.333333 | 0.666667 | 0.833333 | 4.833333 | 0.800000 | 1.200000 | 6.833333 | 7.300000 | 0.538462 | 0.518325 | 0.836735 | 1.000000 | 0.222857 | 0.755396 | 0.648148 | 0.554859 | 0.700605 | 0.833333 | 0.592593 | 0.198020 | 0.206612 | 0.555556 | 0.205128 | 0.315789 | 0.416667 | 0.321586 |
| 2264 | Eugene Jackson | Orthodox | No | No | No | No | Eugene Jackson | 0.000000 | 0.500000 | 0.625000 | 0.000000 | 0.166667 | 0.625000 | 0.250000 | 0.000000 | 0.375000 | 0.250000 | 0.250000 | 0.875000 | 3.875000 | 12.750000 | 16.375000 | 0.625000 | 3.375000 | 0.500000 | 0.000000 | 3.125000 | 0.250000 | 0.500000 | 0.250000 | 0.750000 | 0.750000 | 0.000000 | 2.368750 | 1.500000 | 1.125000 | 0.625000 | 1.500000 | 0.000000 | 1.750000 | 3.250000 | 5.250000 | 13.375000 | 16.125000 | 1.000000 | 2.750000 | 1.125000 | 1.375000 | 2.500000 | 0.000000 | 2.750000 | 0.250000 | 1.250000 | 0.000000 | 0.750000 | 1.000000 | 0.000000 | 1.000000 | 2.000000 | 2.750000 | 0.800000 | 0.185185 | 0.500000 | NaN | 0.120000 | 1.000000 | 0.500000 | 0.225806 | 0.778626 | 0.250000 | 0.454545 | 0.000000 | 0.545455 | 0.400000 | NaN | 0.363636 | 0.380952 | 0.170543 |
| 2286 | Fabiano Iha | Orthodox | No | No | No | No | Fabiano Iha | 0.000000 | 0.733333 | 0.933333 | 0.000000 | 0.961111 | 3.200000 | 0.466667 | 1.200000 | 1.866667 | 1.133333 | 1.866667 | 4.866667 | 9.800000 | 8.466667 | 13.800000 | 1.266667 | 7.600000 | 0.800000 | 1.400000 | 4.933333 | 2.000000 | 2.866667 | 0.000000 | 0.466667 | 0.000000 | 0.000000 | 0.723333 | 4.333333 | 0.266667 | 0.400000 | 1.533333 | 1.800000 | 1.666667 | 5.000000 | 9.400000 | 10.933333 | 15.733333 | 1.066667 | 8.533333 | 0.266667 | 0.600000 | 4.000000 | 2.733333 | 2.666667 | 0.600000 | 4.200000 | 0.000000 | 0.200000 | 2.466667 | 0.933333 | 1.000000 | 4.400000 | 4.800000 | 0.578947 | 0.421053 | 0.583333 | 0.857143 | 0.378378 | 0.566667 | 0.651163 | 0.496599 | 0.613527 | 0.562500 | 0.492188 | 0.000000 | 0.333333 | 0.616667 | 0.341463 | 0.375000 | 0.468085 | 0.305085 |
1218 rows × 78 columns
# Calculate the mean for each of the specified columns
means3 = fa_non_champions[metrics1].mean()
# Rank the means from highest to lowest and plot again
sorted_means3 = means3.sort_values(ascending=False)
# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_means3.plot(kind='bar', color='chartreuse')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90) # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()
# Display max rows
pd.set_option('display.max_rows', None)
print(sorted_means3)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)
RA_STR 22.930341 RA_SIG 15.091705 RA_DISTANCE 11.018353 RA_HEAD 9.655423 RA_BODY 3.090213 RA_LEG 2.346069 RA_CLINCH 2.088535 RA_GROUND 1.984818 RA_CTRL 0.943873 RA_TD 0.435841 RA_SUB.ATT 0.160861 RA_KD 0.079756 RA_REV 0.064868 dtype: float64
# Calculate the mean for each of the specified columns
meansC = fa_non_champions[metrics2].mean()
# Rank the means from highest to lowest and plot again
sorted_meansC = meansC.sort_values(ascending=False)
# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_meansC.plot(kind='bar', color='skyblue')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)
plt.show()
# Display max rows
pd.set_option('display.max_rows', None)
print(sorted_meansC)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)
RA_LEG_% 0.808193 RA_BODY_% 0.696145 RA_GROUND_% 0.685055 RA_CLINCH_% 0.683619 RA_HEAD_DEF_% 0.625707 RA_DISTANCE_DEF_% 0.607792 RA_TD_DEF_% 0.595034 RA_SIG_DEF_% 0.543268 RA_STR_% 0.533661 RA_STR_DEF_% 0.454400 RA_SIG_% 0.443776 RA_DISTANCE_% 0.382282 RA_TD_% 0.374455 RA_HEAD_% 0.361605 RA_CLINCH_DEF_% 0.309204 RA_BODY_DEF_% 0.302528 RA_GROUND_DEF_% 0.291589 RA_LEG_DEF_% 0.186460 dtype: float64
fa_non_challengers = fa[fa['EVER_CHALLENGER'] != 'Yes']
fa_non_challengers
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | FA_FIGHTER | FA_KD | FA_TD | FA_SUB.ATT | FA_REV | FA_CTRL | FA_HEAD | FA_BODY | FA_LEG | FA_DISTANCE | FA_CLINCH | FA_GROUND | FA_SIG | FA_TOT.SIG | FA_STR | FA_TOTAL.STR | FA_TOTAL.TD | FA_TOTAL.HEAD | FA_TOTAL.BODY | FA_TOTAL.LEG | FA_TOTAL.DISTANCE | FA_TOTAL.CLINCH | FA_TOTAL.GROUND | FA_OPP_KD | FA_OPP_TD | FA_OPP_SUB.ATT | FA_OPP_REV | FA_OPP_CTRL | FA_OPP_HEAD | FA_OPP_BODY | FA_OPP_LEG | FA_OPP_DISTANCE | FA_OPP_CLINCH | FA_OPP_GROUND | FA_OPP_SIG | FA_OPP_TOT.SIG | FA_OPP_STR | FA_OPP_TOTAL.STR | FA_OPP_TOTAL.TD | FA_OPP_TOTAL.HEAD | FA_OPP_TOTAL.BODY | FA_OPP_TOTAL.LEG | FA_OPP_TOTAL.DISTANCE | FA_OPP_TOTAL.CLINCH | FA_OPP_TOTAL.GROUND | FA_TD_DEF | FA_HEAD_DEF | FA_BODY_DEF | FA_LEG_DEF | FA_DISTANCE_DEF | FA_CLINCH_DEF | FA_GROUND_DEF | FA_SIG_DEF | FA_STR_DEF | FA_TD_% | FA_HEAD_% | FA_BODY_% | FA_LEG_% | FA_DISTANCE_% | FA_CLINCH_% | FA_GROUND_% | FA_SIG_% | FA_STR_% | FA_TD_DEF_% | FA_HEAD_DEF_% | FA_BODY_DEF_% | FA_LEG_DEF_% | FA_DISTANCE_DEF_% | FA_CLINCH_DEF_% | FA_GROUND_DEF_% | FA_SIG_DEF_% | FA_STR_DEF_% | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | CJ Vergara | Orthodox | No | No | No | No | CJ Vergara | 0.000000 | 0.000000 | 0.083333 | 0.083333 | 0.694444 | 13.222222 | 8.861111 | 3.194444 | 17.111111 | 2.027778 | 6.138889 | 25.277778 | 43.722222 | 35.694444 | 55.388889 | 0.000000 | 28.388889 | 11.694444 | 3.638889 | 31.944444 | 2.694444 | 9.083333 | 0.166667 | 1.000000 | 0.388889 | 0.111111 | 1.316204 | 13.611111 | 5.500000 | 2.944444 | 19.027778 | 1.527778 | 1.500000 | 22.055556 | 46.583333 | 26.611111 | 52.277778 | 2.500000 | 36.055556 | 7.333333 | 3.194444 | 42.500000 | 2.083333 | 2.000000 | 1.500000 | 22.444444 | 1.833333 | 0.250000 | 23.472222 | 0.555556 | 0.500000 | 24.527778 | 25.666667 | NaN | 0.465753 | 0.757720 | 0.877863 | 0.535652 | 0.752577 | 0.675841 | 0.578145 | 0.644433 | 0.600000 | 0.622496 | 0.250000 | 0.078261 | 0.552288 | 0.266667 | 0.250000 | 0.526535 | 0.490967 |
| 2 | Curtis Blaydes | Orthodox | No | No | No | No | Curtis Blaydes | 0.055556 | 1.266667 | 0.000000 | 0.000000 | 1.519198 | 9.433333 | 1.475926 | 2.385185 | 6.462963 | 1.612963 | 5.218519 | 13.294444 | 27.970370 | 22.492593 | 39.153704 | 2.444444 | 23.518519 | 1.692593 | 2.759259 | 19.392593 | 2.029630 | 6.548148 | 0.157407 | 0.344444 | 0.037037 | 0.000000 | 0.201883 | 6.401852 | 1.224074 | 0.727778 | 6.603704 | 1.101852 | 0.648148 | 8.353704 | 19.874074 | 12.364815 | 24.161111 | 0.485185 | 17.364815 | 1.670370 | 0.838889 | 17.751852 | 1.344444 | 0.777778 | 0.140741 | 10.962963 | 0.446296 | 0.111111 | 11.148148 | 0.242593 | 0.129630 | 11.520370 | 11.796296 | 0.518182 | 0.401102 | 0.871991 | 0.864430 | 0.333270 | 0.794708 | 0.796946 | 0.475305 | 0.574469 | 0.290076 | 0.631332 | 0.267184 | 0.132450 | 0.627999 | 0.180441 | 0.166667 | 0.579668 | 0.488235 |
| 3 | Jailton Almeida | Orthodox | No | No | No | No | Jailton Almeida | 0.000000 | 2.028571 | 0.685714 | 0.028571 | 3.565476 | 13.557143 | 0.571429 | 0.100000 | 0.428571 | 0.085714 | 13.714286 | 14.228571 | 22.457143 | 26.142857 | 37.014286 | 3.000000 | 21.228571 | 0.857143 | 0.371429 | 1.771429 | 0.228571 | 20.457143 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.037857 | 1.700000 | 0.085714 | 0.142857 | 0.271429 | 0.028571 | 1.628571 | 1.928571 | 3.400000 | 5.800000 | 8.042857 | 0.057143 | 3.085714 | 0.171429 | 0.142857 | 1.142857 | 0.028571 | 2.228571 | 0.057143 | 1.385714 | 0.085714 | 0.000000 | 0.871429 | 0.000000 | 0.600000 | 1.471429 | 2.242857 | 0.676190 | 0.638627 | 0.666667 | 0.269231 | 0.241935 | 0.375000 | 0.670391 | 0.633588 | 0.706291 | 1.000000 | 0.449074 | 0.500000 | 0.000000 | 0.762500 | 0.000000 | 0.269231 | 0.432773 | 0.278863 |
| 4 | Benoit Saint Denis | Southpaw | No | No | No | No | Benoit Saint Denis | 0.357143 | 1.238095 | 0.500000 | 0.142857 | 1.931746 | 10.833333 | 6.380952 | 3.047619 | 11.404762 | 3.642857 | 5.214286 | 20.261905 | 37.119048 | 32.666667 | 51.357143 | 3.238095 | 25.642857 | 8.285714 | 3.190476 | 23.309524 | 4.690476 | 9.119048 | 0.071429 | 0.428571 | 0.428571 | 0.000000 | 0.255952 | 9.857143 | 4.261905 | 1.619048 | 11.976190 | 3.238095 | 0.523810 | 15.738095 | 27.547619 | 18.952381 | 31.571429 | 0.976190 | 21.023810 | 4.785714 | 1.738095 | 22.642857 | 4.380952 | 0.523810 | 0.547619 | 11.166667 | 0.523810 | 0.119048 | 10.666667 | 1.142857 | 0.000000 | 11.809524 | 12.619048 | 0.382353 | 0.422470 | 0.770115 | 0.955224 | 0.489275 | 0.776650 | 0.571802 | 0.545863 | 0.636069 | 0.560976 | 0.531144 | 0.109453 | 0.068493 | 0.471083 | 0.260870 | 0.000000 | 0.428695 | 0.399698 |
| 7 | Jack Della Maddalena | Switch | No | No | No | No | Jack Della Maddalena | 0.761905 | 0.047619 | 0.142857 | 0.000000 | 0.201587 | 18.952381 | 6.571429 | 2.047619 | 21.809524 | 2.380952 | 3.380952 | 27.571429 | 51.142857 | 33.285714 | 57.523810 | 0.142857 | 38.952381 | 9.428571 | 2.761905 | 43.761905 | 2.809524 | 4.571429 | 0.000000 | 0.619048 | 0.142857 | 0.047619 | 0.662698 | 8.666667 | 3.476190 | 3.571429 | 14.809524 | 0.857143 | 0.047619 | 15.714286 | 49.190476 | 16.666667 | 50.238095 | 2.000000 | 38.619048 | 6.428571 | 4.142857 | 48.000000 | 1.142857 | 0.047619 | 1.380952 | 29.952381 | 2.952381 | 0.571429 | 33.190476 | 0.285714 | 0.000000 | 33.476190 | 33.571429 | 0.333333 | 0.486553 | 0.696970 | 0.741379 | 0.498368 | 0.847458 | 0.739583 | 0.539106 | 0.578642 | 0.690476 | 0.775586 | 0.459259 | 0.137931 | 0.691468 | 0.250000 | 0.000000 | 0.680542 | 0.668246 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2198 | Trevor Prangley | Orthodox | No | No | No | No | Trevor Prangley | 0.000000 | 1.250000 | 0.416667 | 0.000000 | 2.414583 | 3.833333 | 2.916667 | 3.000000 | 1.833333 | 4.833333 | 3.083333 | 9.750000 | 16.416667 | 26.125000 | 34.375000 | 2.000000 | 10.333333 | 3.083333 | 3.000000 | 6.041667 | 5.500000 | 4.875000 | 0.000000 | 0.583333 | 0.083333 | 0.000000 | 1.219444 | 3.333333 | 2.291667 | 1.000000 | 2.583333 | 2.416667 | 1.625000 | 6.625000 | 13.291667 | 18.875000 | 28.541667 | 2.250000 | 9.333333 | 2.958333 | 1.000000 | 5.375000 | 4.250000 | 3.666667 | 1.666667 | 6.000000 | 0.666667 | 0.000000 | 2.791667 | 1.833333 | 2.041667 | 6.666667 | 9.666667 | 0.625000 | 0.370968 | 0.945946 | 1.000000 | 0.303448 | 0.878788 | 0.632479 | 0.593909 | 0.760000 | 0.740741 | 0.642857 | 0.225352 | 0.000000 | 0.519380 | 0.431373 | 0.556818 | 0.501567 | 0.338686 |
| 2234 | Wesley Correira | Orthodox | No | No | No | No | Wesley Correira | 0.250000 | 0.000000 | 0.000000 | 0.000000 | 0.056250 | 8.750000 | 0.625000 | 0.375000 | 4.500000 | 5.000000 | 0.250000 | 9.750000 | 23.000000 | 17.875000 | 31.125000 | 0.125000 | 21.500000 | 0.625000 | 0.875000 | 13.625000 | 9.125000 | 0.250000 | 0.125000 | 0.000000 | 0.000000 | 0.000000 | 0.779167 | 22.375000 | 1.875000 | 2.875000 | 16.875000 | 10.250000 | 0.000000 | 27.125000 | 45.625000 | 32.000000 | 50.875000 | 1.125000 | 39.250000 | 2.500000 | 3.875000 | 29.375000 | 16.250000 | 0.000000 | 1.125000 | 16.875000 | 0.625000 | 1.000000 | 12.500000 | 6.000000 | 0.000000 | 18.500000 | 18.875000 | 0.000000 | 0.406977 | 1.000000 | 0.428571 | 0.330275 | 0.547945 | 1.000000 | 0.423913 | 0.574297 | 1.000000 | 0.429936 | 0.250000 | 0.258065 | 0.425532 | 0.369231 | NaN | 0.405479 | 0.371007 |
| 2252 | Ian Freeman | Orthodox | No | No | No | No | Ian Freeman | 0.000000 | 0.233333 | 0.066667 | 0.200000 | 1.591111 | 9.900000 | 1.366667 | 0.533333 | 1.300000 | 3.500000 | 7.000000 | 11.800000 | 21.266667 | 23.166667 | 33.066667 | 0.433333 | 19.100000 | 1.633333 | 0.533333 | 5.833333 | 4.633333 | 10.800000 | 0.200000 | 0.433333 | 0.600000 | 0.166667 | 1.223333 | 3.666667 | 2.700000 | 3.200000 | 3.866667 | 3.100000 | 2.600000 | 9.566667 | 16.400000 | 15.400000 | 22.700000 | 2.600000 | 9.000000 | 3.366667 | 4.033333 | 8.700000 | 3.900000 | 3.800000 | 2.166667 | 5.333333 | 0.666667 | 0.833333 | 4.833333 | 0.800000 | 1.200000 | 6.833333 | 7.300000 | 0.538462 | 0.518325 | 0.836735 | 1.000000 | 0.222857 | 0.755396 | 0.648148 | 0.554859 | 0.700605 | 0.833333 | 0.592593 | 0.198020 | 0.206612 | 0.555556 | 0.205128 | 0.315789 | 0.416667 | 0.321586 |
| 2264 | Eugene Jackson | Orthodox | No | No | No | No | Eugene Jackson | 0.000000 | 0.500000 | 0.625000 | 0.000000 | 0.166667 | 0.625000 | 0.250000 | 0.000000 | 0.375000 | 0.250000 | 0.250000 | 0.875000 | 3.875000 | 12.750000 | 16.375000 | 0.625000 | 3.375000 | 0.500000 | 0.000000 | 3.125000 | 0.250000 | 0.500000 | 0.250000 | 0.750000 | 0.750000 | 0.000000 | 2.368750 | 1.500000 | 1.125000 | 0.625000 | 1.500000 | 0.000000 | 1.750000 | 3.250000 | 5.250000 | 13.375000 | 16.125000 | 1.000000 | 2.750000 | 1.125000 | 1.375000 | 2.500000 | 0.000000 | 2.750000 | 0.250000 | 1.250000 | 0.000000 | 0.750000 | 1.000000 | 0.000000 | 1.000000 | 2.000000 | 2.750000 | 0.800000 | 0.185185 | 0.500000 | NaN | 0.120000 | 1.000000 | 0.500000 | 0.225806 | 0.778626 | 0.250000 | 0.454545 | 0.000000 | 0.545455 | 0.400000 | NaN | 0.363636 | 0.380952 | 0.170543 |
| 2286 | Fabiano Iha | Orthodox | No | No | No | No | Fabiano Iha | 0.000000 | 0.733333 | 0.933333 | 0.000000 | 0.961111 | 3.200000 | 0.466667 | 1.200000 | 1.866667 | 1.133333 | 1.866667 | 4.866667 | 9.800000 | 8.466667 | 13.800000 | 1.266667 | 7.600000 | 0.800000 | 1.400000 | 4.933333 | 2.000000 | 2.866667 | 0.000000 | 0.466667 | 0.000000 | 0.000000 | 0.723333 | 4.333333 | 0.266667 | 0.400000 | 1.533333 | 1.800000 | 1.666667 | 5.000000 | 9.400000 | 10.933333 | 15.733333 | 1.066667 | 8.533333 | 0.266667 | 0.600000 | 4.000000 | 2.733333 | 2.666667 | 0.600000 | 4.200000 | 0.000000 | 0.200000 | 2.466667 | 0.933333 | 1.000000 | 4.400000 | 4.800000 | 0.578947 | 0.421053 | 0.583333 | 0.857143 | 0.378378 | 0.566667 | 0.651163 | 0.496599 | 0.613527 | 0.562500 | 0.492188 | 0.000000 | 0.333333 | 0.616667 | 0.341463 | 0.375000 | 0.468085 | 0.305085 |
1108 rows × 78 columns
# Calculate the mean for each of the specified columns
means4 = fa_non_challengers[metrics1].mean()
# Rank the means from highest to lowest and plot again
sorted_means4 = means4.sort_values(ascending=False)
# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_means4.plot(kind='bar', color='slategray')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90) # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()
# Display max rows
pd.set_option('display.max_rows', None)
print(sorted_means4)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)
RA_STR 23.061549 RA_SIG 15.224483 RA_DISTANCE 11.119175 RA_HEAD 9.721662 RA_BODY 3.125167 RA_LEG 2.377654 RA_CLINCH 2.121820 RA_GROUND 1.983488 RA_CTRL 0.950932 RA_TD 0.444311 RA_SUB.ATT 0.162821 RA_KD 0.079236 RA_REV 0.066557 dtype: float64
# Calculate the mean for each of the specified columns
meansD = fa_non_challengers[metrics2].mean()
# Rank the means from highest to lowest and plot again
sorted_meansD = meansD.sort_values(ascending=False)
# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_meansD.plot(kind='bar', color='darksalmon')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)
plt.show()
# Display max rows
pd.set_option('display.max_rows', None)
print(sorted_meansD)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)
RA_LEG_% 0.809756 RA_BODY_% 0.696210 RA_GROUND_% 0.685475 RA_CLINCH_% 0.683458 RA_HEAD_DEF_% 0.626455 RA_DISTANCE_DEF_% 0.608378 RA_TD_DEF_% 0.596982 RA_SIG_DEF_% 0.544152 RA_STR_% 0.534572 RA_STR_DEF_% 0.455948 RA_SIG_% 0.445700 RA_DISTANCE_% 0.384535 RA_TD_% 0.378139 RA_HEAD_% 0.363016 RA_CLINCH_DEF_% 0.308838 RA_BODY_DEF_% 0.304807 RA_GROUND_DEF_% 0.292621 RA_LEG_DEF_% 0.187336 dtype: float64
round_average.describe()
| RA_KD | RA_TD | RA_SUB.ATT | RA_REV | RA_CTRL | RA_HEAD | RA_BODY | RA_LEG | RA_DISTANCE | RA_CLINCH | RA_GROUND | RA_SIG | RA_TOT.SIG | RA_STR | RA_TOTAL.STR | RA_TOTAL.TD | RA_TOTAL.HEAD | RA_TOTAL.BODY | RA_TOTAL.LEG | RA_TOTAL.DISTANCE | RA_TOTAL.CLINCH | RA_TOTAL.GROUND | RA_OPP_KD | RA_OPP_TD | RA_OPP_SUB.ATT | RA_OPP_REV | RA_OPP_CTRL | RA_OPP_HEAD | RA_OPP_BODY | RA_OPP_LEG | RA_OPP_DISTANCE | RA_OPP_CLINCH | RA_OPP_GROUND | RA_OPP_SIG | RA_OPP_TOT.SIG | RA_OPP_STR | RA_OPP_TOTAL.STR | RA_OPP_TOTAL.TD | RA_OPP_TOTAL.HEAD | RA_OPP_TOTAL.BODY | RA_OPP_TOTAL.LEG | RA_OPP_TOTAL.DISTANCE | RA_OPP_TOTAL.CLINCH | RA_OPP_TOTAL.GROUND | RA_TD_DEF | RA_HEAD_DEF | RA_BODY_DEF | RA_LEG_DEF | RA_DISTANCE_DEF | RA_CLINCH_DEF | RA_GROUND_DEF | RA_SIG_DEF | RA_STR_DEF | RA_TD_% | RA_HEAD_% | RA_BODY_% | RA_LEG_% | RA_DISTANCE_% | RA_CLINCH_% | RA_GROUND_% | RA_SIG_% | RA_STR_% | RA_TD_DEF_% | RA_HEAD_DEF_% | RA_BODY_DEF_% | RA_LEG_DEF_% | RA_DISTANCE_DEF_% | RA_CLINCH_DEF_% | RA_GROUND_DEF_% | RA_SIG_DEF_% | RA_STR_DEF_% | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2317.000000 | 2085.000000 | 2306.000000 | 2229.000000 | 2191.000000 | 2308.000000 | 2176.000000 | 1977.000000 | 2313.000000 | 2316.000000 | 2130.000000 | 2311.000000 | 2247.000000 | 2188.000000 | 2307.000000 | 2185.000000 | 2162.000000 | 2314.000000 | 2315.000000 |
| mean | 0.067797 | 0.404869 | 0.157241 | 0.063200 | 0.875441 | 8.391623 | 2.795283 | 2.185652 | 9.709796 | 1.927536 | 1.735226 | 13.372558 | 30.830175 | 20.749173 | 39.285843 | 1.177554 | 24.018487 | 4.065400 | 2.746289 | 25.453742 | 2.829491 | 2.546942 | 0.117523 | 0.524601 | 0.226220 | 0.065894 | 1.130002 | 10.225281 | 3.123706 | 2.325865 | 10.564688 | 2.257289 | 2.852874 | 15.674851 | 33.588964 | 24.693330 | 43.925761 | 1.228895 | 26.322038 | 4.387894 | 2.879031 | 26.264392 | 3.202086 | 4.122485 | 0.704294 | 16.096757 | 1.264188 | 0.553167 | 15.699703 | 0.944797 | 1.269611 | 17.914112 | 19.232431 | 0.362632 | 0.344802 | 0.685877 | 0.797539 | 0.371917 | 0.667060 | 0.678988 | 0.434416 | 0.530726 | 0.560937 | 0.604859 | 0.288381 | 0.188519 | 0.604290 | 0.305324 | 0.296480 | 0.524229 | 0.433616 |
| std | 0.120949 | 0.422592 | 0.255422 | 0.124811 | 0.722091 | 5.174244 | 2.054144 | 1.899629 | 6.613471 | 1.735839 | 2.258217 | 7.325111 | 15.655065 | 9.801365 | 16.820862 | 1.031576 | 12.901843 | 2.831087 | 2.359073 | 15.272053 | 2.379719 | 3.243287 | 0.206597 | 0.491388 | 0.370242 | 0.143250 | 0.837123 | 5.348792 | 2.105324 | 1.882882 | 6.929573 | 2.167954 | 3.350068 | 7.169161 | 14.881281 | 9.979317 | 15.464867 | 0.919139 | 12.326305 | 2.761631 | 2.298362 | 15.508892 | 2.895568 | 4.804211 | 0.645343 | 8.435776 | 0.980268 | 0.637422 | 9.427357 | 0.971258 | 1.721470 | 9.182353 | 9.081351 | 0.254298 | 0.123427 | 0.174944 | 0.172720 | 0.119646 | 0.194166 | 0.202545 | 0.120789 | 0.133708 | 0.264984 | 0.120708 | 0.156770 | 0.155215 | 0.113059 | 0.171490 | 0.168285 | 0.106589 | 0.120914 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.055556 | 0.000000 | 0.000000 | 0.309259 | 4.750000 | 1.333333 | 0.833333 | 4.611111 | 0.750000 | 0.333333 | 8.194444 | 19.689755 | 14.416667 | 28.020833 | 0.361111 | 15.000000 | 2.000000 | 1.004762 | 13.861111 | 1.208333 | 0.500000 | 0.000000 | 0.166667 | 0.000000 | 0.000000 | 0.492593 | 6.611111 | 1.666667 | 1.000000 | 5.500000 | 0.896581 | 0.777778 | 10.833333 | 23.333333 | 18.430556 | 34.037681 | 0.555556 | 17.755833 | 2.444444 | 1.333333 | 15.000000 | 1.333333 | 1.083333 | 0.236111 | 10.031863 | 0.500000 | 0.118056 | 8.892143 | 0.333333 | 0.240654 | 11.366667 | 12.908182 | 0.189815 | 0.279971 | 0.600000 | 0.732652 | 0.310409 | 0.575257 | 0.589286 | 0.372549 | 0.451613 | 0.397535 | 0.541585 | 0.196846 | 0.090909 | 0.542024 | 0.202050 | 0.198645 | 0.462325 | 0.355427 |
| 50% | 0.000000 | 0.312500 | 0.066667 | 0.000000 | 0.722222 | 7.833333 | 2.361111 | 1.769444 | 8.711111 | 1.500000 | 1.166667 | 12.666667 | 29.586667 | 20.200000 | 38.722222 | 1.000000 | 22.722222 | 3.531746 | 2.194444 | 23.440476 | 2.285714 | 1.715812 | 0.047619 | 0.416667 | 0.111111 | 0.000000 | 0.974074 | 9.416667 | 2.800000 | 2.000000 | 9.611111 | 1.727692 | 1.888889 | 15.000000 | 32.166667 | 23.502885 | 42.597222 | 1.065152 | 24.920455 | 4.002353 | 2.466422 | 24.888319 | 2.502857 | 2.666667 | 0.566667 | 14.909868 | 1.111111 | 0.390048 | 14.750000 | 0.717593 | 0.722222 | 16.855820 | 18.250000 | 0.344828 | 0.346460 | 0.698413 | 0.825664 | 0.376477 | 0.684564 | 0.692908 | 0.436464 | 0.529686 | 0.597580 | 0.619767 | 0.285211 | 0.167332 | 0.606607 | 0.296480 | 0.294118 | 0.534580 | 0.443609 |
| 75% | 0.102273 | 0.611111 | 0.222222 | 0.083333 | 1.266667 | 11.314815 | 3.833333 | 3.000000 | 13.503938 | 2.666667 | 2.333333 | 17.833333 | 41.388889 | 26.530000 | 50.166667 | 1.722222 | 32.261183 | 5.583333 | 3.805556 | 35.712929 | 3.833333 | 3.395672 | 0.144444 | 0.755952 | 0.291667 | 0.083333 | 1.607407 | 12.947751 | 4.154499 | 3.138889 | 14.444444 | 2.966667 | 3.707949 | 19.777778 | 42.224183 | 29.844793 | 53.285714 | 1.666667 | 33.091667 | 5.833333 | 3.916667 | 35.666667 | 4.116667 | 5.333333 | 1.000000 | 20.884568 | 1.750000 | 0.777778 | 21.177778 | 1.233333 | 1.625000 | 23.250000 | 24.666667 | 0.500000 | 0.414784 | 0.784832 | 0.907787 | 0.438253 | 0.787521 | 0.794872 | 0.502606 | 0.608728 | 0.750000 | 0.680792 | 0.372816 | 0.250753 | 0.666667 | 0.388982 | 0.383048 | 0.593063 | 0.516665 |
| max | 1.000000 | 3.500000 | 3.000000 | 1.500000 | 4.891667 | 35.722222 | 15.666667 | 21.000000 | 45.333333 | 15.166667 | 32.000000 | 49.666667 | 98.333333 | 75.000000 | 105.000000 | 7.346970 | 80.333333 | 18.777778 | 24.000000 | 95.500000 | 18.333333 | 49.000000 | 2.000000 | 5.000000 | 5.000000 | 2.000000 | 4.700000 | 52.166667 | 21.000000 | 20.500000 | 66.666667 | 20.500000 | 33.500000 | 68.500000 | 100.000000 | 85.333333 | 112.000000 | 7.333333 | 80.333333 | 23.000000 | 24.500000 | 98.333333 | 30.500000 | 42.166667 | 6.000000 | 72.000000 | 7.777778 | 9.000000 | 73.000000 | 12.000000 | 17.000000 | 73.333333 | 73.333333 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.868852 |
metrics1 = ['RA_KD', 'RA_TD', 'RA_SUB.ATT', 'RA_REV', 'RA_CTRL', 'RA_HEAD', 'RA_BODY', 'RA_LEG', 'RA_DISTANCE',
'RA_CLINCH', 'RA_GROUND', 'RA_SIG', 'RA_STR']
metrics2 = [
'RA_TD_%','RA_HEAD_%','RA_BODY_%','RA_LEG_%','RA_DISTANCE_%','RA_CLINCH_%', 'RA_GROUND_%', 'RA_SIG_%',
'RA_STR_%', 'RA_TD_DEF_%','RA_HEAD_DEF_%','RA_BODY_DEF_%','RA_LEG_DEF_%', 'RA_DISTANCE_DEF_%',
'RA_CLINCH_DEF_%', 'RA_GROUND_DEF_%', 'RA_SIG_DEF_%', 'RA_STR_DEF_%']
# Filter the dataframe using the correct column name "FIGHTER"
ra = round_average[round_average['FIGHTER'].isin(fighters_list)]
ra
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | RA_FIGHTER | RA_KD | RA_TD | RA_SUB.ATT | RA_REV | RA_CTRL | RA_HEAD | RA_BODY | RA_LEG | RA_DISTANCE | RA_CLINCH | RA_GROUND | RA_SIG | RA_TOT.SIG | RA_STR | RA_TOTAL.STR | RA_TOTAL.TD | RA_TOTAL.HEAD | RA_TOTAL.BODY | RA_TOTAL.LEG | RA_TOTAL.DISTANCE | RA_TOTAL.CLINCH | RA_TOTAL.GROUND | RA_OPP_KD | RA_OPP_TD | RA_OPP_SUB.ATT | RA_OPP_REV | RA_OPP_CTRL | RA_OPP_HEAD | RA_OPP_BODY | RA_OPP_LEG | RA_OPP_DISTANCE | RA_OPP_CLINCH | RA_OPP_GROUND | RA_OPP_SIG | RA_OPP_TOT.SIG | RA_OPP_STR | RA_OPP_TOTAL.STR | RA_OPP_TOTAL.TD | RA_OPP_TOTAL.HEAD | RA_OPP_TOTAL.BODY | RA_OPP_TOTAL.LEG | RA_OPP_TOTAL.DISTANCE | RA_OPP_TOTAL.CLINCH | RA_OPP_TOTAL.GROUND | RA_TD_DEF | RA_HEAD_DEF | RA_BODY_DEF | RA_LEG_DEF | RA_DISTANCE_DEF | RA_CLINCH_DEF | RA_GROUND_DEF | RA_SIG_DEF | RA_STR_DEF | RA_TD_% | RA_HEAD_% | RA_BODY_% | RA_LEG_% | RA_DISTANCE_% | RA_CLINCH_% | RA_GROUND_% | RA_SIG_% | RA_STR_% | RA_TD_DEF_% | RA_HEAD_DEF_% | RA_BODY_DEF_% | RA_LEG_DEF_% | RA_DISTANCE_DEF_% | RA_CLINCH_DEF_% | RA_GROUND_DEF_% | RA_SIG_DEF_% | RA_STR_DEF_% | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | CJ Vergara | Orthodox | No | No | No | No | CJ Vergara | 0.000000 | 0.000000 | 0.055556 | 0.055556 | 0.681481 | 12.750000 | 9.861111 | 3.083333 | 18.250000 | 2.500000 | 4.944444 | 25.694444 | 44.611111 | 37.111111 | 57.305556 | 0.000000 | 28.472222 | 12.638889 | 3.500000 | 34.111111 | 3.250000 | 7.250000 | 0.111111 | 1.000000 | 0.305556 | 0.138889 | 1.258796 | 14.000000 | 5.777778 | 2.916667 | 19.722222 | 1.527778 | 1.444444 | 22.694444 | 48.111111 | 27.361111 | 54.027778 | 2.555556 | 37.333333 | 7.638889 | 3.138889 | 44.250000 | 2.027778 | 1.833333 | 1.555556 | 23.333333 | 1.861111 | 0.222222 | 24.527778 | 0.500000 | 0.388889 | 25.416667 | 26.666667 | NaN | 0.447805 | 0.780220 | 0.880952 | 0.535016 | 0.769231 | 0.681992 | 0.575965 | 0.647601 | 0.608696 | 0.625000 | 0.243636 | 0.070796 | 0.554300 | 0.246575 | 0.212121 | 0.528291 | 0.493573 |
| 2 | Curtis Blaydes | Orthodox | No | No | No | No | Curtis Blaydes | 0.024444 | 1.886667 | 0.000000 | 0.000000 | 2.670889 | 8.462222 | 1.548889 | 1.500000 | 5.433333 | 1.900000 | 4.177778 | 11.511111 | 21.655556 | 22.500000 | 35.106667 | 3.837778 | 18.253333 | 1.726667 | 1.675556 | 14.168889 | 2.197778 | 5.288889 | 0.046667 | 0.333333 | 0.026667 | 0.000000 | 0.216815 | 5.788889 | 1.411111 | 0.400000 | 6.882222 | 0.542222 | 0.175556 | 7.600000 | 19.677778 | 26.988889 | 40.008889 | 0.613333 | 17.313333 | 1.906667 | 0.457778 | 18.555556 | 0.902222 | 0.220000 | 0.280000 | 11.524444 | 0.495556 | 0.057778 | 11.673333 | 0.360000 | 0.044444 | 12.077778 | 13.020000 | 0.491604 | 0.463599 | 0.897040 | 0.895225 | 0.383469 | 0.864510 | 0.789916 | 0.531555 | 0.640904 | 0.456522 | 0.665640 | 0.259907 | 0.126214 | 0.629102 | 0.399015 | 0.202020 | 0.613778 | 0.325428 |
| 3 | Jailton Almeida | Orthodox | No | No | No | No | Jailton Almeida | 0.000000 | 1.476190 | 0.304762 | 0.200000 | 3.737937 | 8.580952 | 0.571429 | 0.266667 | 0.647619 | 0.600000 | 8.171429 | 9.419048 | 14.228571 | 27.542857 | 35.980952 | 3.409524 | 12.971429 | 0.628571 | 0.628571 | 1.409524 | 1.600000 | 11.219048 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.198730 | 3.676190 | 0.257143 | 0.028571 | 0.323810 | 0.200000 | 3.438095 | 3.961905 | 7.352381 | 5.923810 | 10.219048 | 0.400000 | 6.809524 | 0.514286 | 0.028571 | 1.933333 | 0.200000 | 5.219048 | 0.400000 | 3.133333 | 0.257143 | 0.000000 | 1.609524 | 0.000000 | 1.780952 | 3.390476 | 4.295238 | 0.432961 | 0.661527 | 0.909091 | 0.424242 | 0.459459 | 0.375000 | 0.728353 | 0.661981 | 0.765484 | 1.000000 | 0.460140 | 0.500000 | 0.000000 | 0.832512 | 0.000000 | 0.341241 | 0.461140 | 0.420317 |
| 4 | Benoit Saint Denis | Southpaw | No | No | No | No | Benoit Saint Denis | 0.209524 | 0.895238 | 0.257143 | 0.047619 | 1.433492 | 11.952381 | 6.561905 | 2.961905 | 14.514286 | 2.761905 | 4.200000 | 21.476190 | 39.304762 | 29.761905 | 49.047619 | 3.780952 | 28.190476 | 8.057143 | 3.057143 | 28.009524 | 3.504762 | 7.790476 | 0.066667 | 0.209524 | 0.323810 | 0.000000 | 0.547302 | 11.952381 | 6.828571 | 2.657143 | 16.561905 | 3.580952 | 1.295238 | 21.438095 | 38.009524 | 25.419048 | 42.790476 | 0.676190 | 27.085714 | 7.885714 | 3.038095 | 31.771429 | 4.942857 | 1.295238 | 0.466667 | 15.133333 | 1.057143 | 0.380952 | 15.209524 | 1.361905 | 0.000000 | 16.571429 | 17.371429 | 0.236776 | 0.423986 | 0.814421 | 0.968847 | 0.518191 | 0.788043 | 0.539120 | 0.546402 | 0.606796 | 0.690141 | 0.558720 | 0.134058 | 0.125392 | 0.478717 | 0.275530 | 0.000000 | 0.435981 | 0.405965 |
| 5 | Dustin Poirier | Southpaw | No | Yes | Yes | Yes | Dustin Poirier | 0.104762 | 0.357143 | 0.340476 | 0.032381 | 0.937897 | 19.855238 | 2.094762 | 1.622857 | 18.732857 | 2.650000 | 2.190000 | 23.572857 | 43.810476 | 29.180000 | 49.939524 | 0.991429 | 39.138095 | 2.737143 | 1.935238 | 36.829524 | 3.870476 | 3.110476 | 0.022857 | 0.444762 | 0.381905 | 0.025714 | 0.798706 | 12.851905 | 4.429524 | 1.852381 | 15.667143 | 2.081905 | 1.384762 | 19.133810 | 41.794286 | 25.088571 | 48.283810 | 1.320952 | 33.875238 | 5.760952 | 2.158095 | 37.068571 | 3.022857 | 1.702857 | 0.876190 | 21.023333 | 1.331429 | 0.305714 | 21.401429 | 0.940952 | 0.318095 | 22.660476 | 23.195238 | 0.360231 | 0.507312 | 0.765310 | 0.838583 | 0.508637 | 0.684670 | 0.704072 | 0.538064 | 0.584307 | 0.663302 | 0.620611 | 0.231113 | 0.141659 | 0.577347 | 0.311279 | 0.186801 | 0.542191 | 0.480394 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2252 | Ian Freeman | Orthodox | No | No | No | No | Ian Freeman | 0.000000 | 0.244444 | 0.166667 | 0.066667 | 1.942407 | 9.344444 | 0.644444 | 0.311111 | 1.122222 | 2.844444 | 6.333333 | 10.300000 | 21.322222 | 22.833333 | 34.544444 | 0.644444 | 20.166667 | 0.844444 | 0.311111 | 5.377778 | 4.722222 | 11.222222 | 0.066667 | 0.266667 | 0.200000 | 0.177778 | 1.447778 | 3.822222 | 2.933333 | 2.611111 | 2.511111 | 3.622222 | 3.233333 | 9.366667 | 16.200000 | 17.966667 | 25.333333 | 2.488889 | 8.922222 | 3.966667 | 3.311111 | 5.755556 | 5.522222 | 4.922222 | 2.222222 | 5.100000 | 1.033333 | 0.700000 | 3.244444 | 1.900000 | 1.688889 | 6.833333 | 7.366667 | 0.379310 | 0.463361 | 0.763158 | 1.000000 | 0.208678 | 0.602353 | 0.564356 | 0.483064 | 0.660984 | 0.892857 | 0.571606 | 0.260504 | 0.211409 | 0.563707 | 0.344064 | 0.343115 | 0.421811 | 0.290789 |
| 2264 | Eugene Jackson | Orthodox | No | No | No | No | Eugene Jackson | 0.000000 | 0.250000 | 0.750000 | 0.000000 | 0.112500 | 0.750000 | 0.125000 | 0.000000 | 0.625000 | 0.125000 | 0.125000 | 0.875000 | 3.250000 | 9.875000 | 12.625000 | 0.750000 | 3.000000 | 0.250000 | 0.000000 | 2.875000 | 0.125000 | 0.250000 | 0.125000 | 0.875000 | 0.500000 | 0.000000 | 1.822917 | 1.375000 | 0.625000 | 0.750000 | 1.625000 | 0.000000 | 1.125000 | 2.750000 | 4.000000 | 11.000000 | 12.625000 | 1.000000 | 2.250000 | 0.625000 | 1.125000 | 2.125000 | 0.000000 | 1.875000 | 0.125000 | 0.875000 | 0.000000 | 0.375000 | 0.500000 | 0.000000 | 0.750000 | 1.250000 | 1.625000 | 0.333333 | 0.250000 | 0.500000 | NaN | 0.217391 | 1.000000 | 0.500000 | 0.269231 | 0.782178 | 0.125000 | 0.388889 | 0.000000 | 0.333333 | 0.235294 | NaN | 0.400000 | 0.312500 | 0.128713 |
| 2278 | Pat Miletich | Orthodox | Yes | No | Yes | Yes | Pat Miletich | 0.083333 | 0.433333 | 0.233333 | 0.000000 | 0.985556 | 4.483333 | 0.683333 | 1.600000 | 4.766667 | 0.950000 | 1.050000 | 6.766667 | 13.150000 | 17.816667 | 24.683333 | 0.433333 | 10.650000 | 0.766667 | 1.733333 | 10.483333 | 1.316667 | 1.350000 | 0.000000 | 0.633333 | 0.483333 | 0.000000 | 0.905000 | 2.133333 | 1.050000 | 1.066667 | 2.066667 | 0.766667 | 1.416667 | 4.250000 | 12.216667 | 12.550000 | 21.716667 | 1.066667 | 9.783333 | 1.216667 | 1.216667 | 7.933333 | 1.250000 | 3.033333 | 0.433333 | 7.650000 | 0.166667 | 0.150000 | 5.866667 | 0.483333 | 1.616667 | 7.966667 | 9.166667 | 1.000000 | 0.420970 | 0.891304 | 0.923077 | 0.454690 | 0.721519 | 0.777778 | 0.514575 | 0.721810 | 0.406250 | 0.781942 | 0.136986 | 0.123288 | 0.739496 | 0.386667 | 0.532967 | 0.652115 | 0.422103 |
| 2282 | Kevin Randleman | Orthodox | Yes | No | Yes | Yes | Kevin Randleman | 0.000000 | 0.283333 | 0.150000 | 0.000000 | 1.512778 | 3.600000 | 0.333333 | 0.066667 | 1.566667 | 0.783333 | 1.650000 | 4.000000 | 9.250000 | 10.850000 | 16.666667 | 0.500000 | 8.850000 | 0.333333 | 0.066667 | 5.016667 | 1.050000 | 3.183333 | 0.050000 | 0.066667 | 0.383333 | 0.000000 | 0.198333 | 1.500000 | 0.583333 | 0.850000 | 0.900000 | 1.033333 | 1.000000 | 2.933333 | 8.016667 | 7.366667 | 12.683333 | 0.066667 | 6.383333 | 0.650000 | 0.983333 | 5.333333 | 1.233333 | 1.450000 | 0.000000 | 4.883333 | 0.066667 | 0.133333 | 4.433333 | 0.200000 | 0.450000 | 5.083333 | 5.316667 | 0.566667 | 0.406780 | 1.000000 | 1.000000 | 0.312292 | 0.746032 | 0.518325 | 0.432432 | 0.651000 | 0.000000 | 0.765013 | 0.102564 | 0.135593 | 0.831250 | 0.162162 | 0.310345 | 0.634096 | 0.419185 |
| 2286 | Fabiano Iha | Orthodox | No | No | No | No | Fabiano Iha | 0.000000 | 0.600000 | 0.400000 | 0.000000 | 1.698889 | 3.200000 | 0.200000 | 1.733333 | 1.333333 | 1.933333 | 1.866667 | 5.133333 | 12.466667 | 13.933333 | 23.266667 | 1.533333 | 9.733333 | 0.933333 | 1.800000 | 5.733333 | 3.600000 | 3.133333 | 0.000000 | 0.200000 | 0.000000 | 0.000000 | 1.661111 | 8.866667 | 0.133333 | 0.133333 | 2.066667 | 6.200000 | 0.866667 | 9.133333 | 15.000000 | 26.400000 | 33.066667 | 0.400000 | 14.666667 | 0.133333 | 0.200000 | 4.800000 | 8.866667 | 1.333333 | 0.200000 | 5.800000 | 0.000000 | 0.066667 | 2.733333 | 2.666667 | 0.466667 | 5.866667 | 6.666667 | 0.391304 | 0.328767 | 0.214286 | 0.962963 | 0.232558 | 0.537037 | 0.595745 | 0.411765 | 0.598854 | 0.500000 | 0.395455 | 0.000000 | 0.333333 | 0.569444 | 0.300752 | 0.350000 | 0.391111 | 0.201613 |
1321 rows × 78 columns
# Original metrics list
original_metrics = [
"KD", "TD", "SUB.ATT", "REV", "CTRL", "HEAD", "BODY", "LEG", "DISTANCE",
"CLINCH", "GROUND", "SIG", "STR", "TD_DEF", "HEAD_DEF", "BODY_DEF", "LEG_DEF",
"DISTANCE_DEF", "CLINCH_DEF", "GROUND_DEF", "SIG_DEF", "STR_DEF", "TD_%",
"HEAD_%", "BODY_%", "LEG_%", "DISTANCE_%", "CLINCH_%", "GROUND_%", "SIG_%",
"STR_%", "TD_DEF_%", "HEAD_DEF_%", "BODY_DEF_%", "LEG_DEF_%", "DISTANCE_DEF_%",
"CLINCH_DEF_%", "GROUND_DEF_%", "SIG_DEF_%", "STR_DEF_%"
]
# Adding 'RA_' prefix to each metric in the list
ra_metrics = ["RA_" + metric for metric in original_metrics]
ra_metrics
# Reinitializing the dictionary to hold the top ten fighters for the updated list of metrics
top_fighters_per_metric = {}
# Creating a dictionary to hold the top ten fighters for each metric
top_fighters_per_metric = {}
# Finding the top ten fighters for each metric
for metric in ra_metrics:
# Some metrics may have missing values, so we sort only non-NA values
top_fighters_per_metric[metric] = ra.nlargest(10, metric)[['FIGHTER', metric]]
# Printing the top ten fighters for each metric with a blank line in between
for metric, data in top_fighters_per_metric.items():
print(f"Top ten fighters for {metric}:")
if isinstance(data, pd.DataFrame):
print(data.to_string(index=False))
else:
print(data)
print("\n") # Blank line for separation
Top ten fighters for RA_KD:
FIGHTER RA_KD
Sergei Pavlovich 0.875000
Santiago Ponzinibbio 0.647839
Uros Medic 0.622222
Chris Daukaus 0.562500
Michael Kuiper 0.555556
James Irvin 0.550000
Ihor Potieria 0.511111
Joachim Christensen 0.500000
Montel Jackson 0.481481
Khalil Rountree Jr. 0.458333
Top ten fighters for RA_TD:
FIGHTER RA_TD
Merab Dvalishvili 2.446970
Jason Witt 2.222222
Jacob Malkoun 2.194444
Jake O'Brien 2.142857
Bartosz Fabinski 2.111111
Jeremy Kennedy 2.027778
Muhammad Mokaev 1.966667
Curtis Blaydes 1.886667
Sean Sherk 1.858081
Mateusz Gamrot 1.781667
Top ten fighters for RA_SUB.ATT:
FIGHTER RA_SUB.ATT
John Albert 1.700000
TJ Waldburger 1.305556
Paul Sass 1.200000
James Wilks 1.111111
Preston Parsons 1.000000
Jason High 0.988889
Jimmy Flick 0.875000
Dave Menne 0.853333
Tatsuro Taira 0.800000
Jimmy Crute 0.777778
Top ten fighters for RA_REV:
FIGHTER RA_REV
Cameron Saaiman 0.694444
Khama Worthy 0.666667
Jiri Prochazka 0.650000
Noad Lahat 0.527778
Chris Wade 0.514286
Jerrod Sanders 0.500000
Yosdenis Cedeno 0.500000
Alvin Robinson 0.500000
Chase Hooper 0.483333
Justin Jaynes 0.466667
Top ten fighters for RA_CTRL:
FIGHTER RA_CTRL
Jailton Almeida 3.737937
Bartosz Fabinski 3.549074
Gregor Gillespie 3.541250
Chael Sonnen 3.321349
Rinat Fakhretdinov 3.225463
Tatiana Suarez 3.023016
Jacob Malkoun 2.949074
Philip De Fries 2.912778
Umar Nurmagomedov 2.885556
Joe Solecki 2.836508
Top ten fighters for RA_HEAD:
FIGHTER RA_HEAD
Sean O'Malley 31.758333
Casey O'Neill 30.166667
Sean Strickland 26.354603
Frank Camacho 26.077778
Max Holloway 25.740825
Khamzat Chimaev 25.333333
Daniel Rodriguez 25.111111
Ryan Janes 24.305556
Danaa Batgerel 24.000000
Parker Porter 23.571429
Top ten fighters for RA_BODY:
FIGHTER RA_BODY
Paulo Costa 11.247222
Anthony Hamilton 10.700000
Kai Kamaka 10.333333
Tony Kelley 9.944444
CJ Vergara 9.861111
Michel Pereira 9.833333
Sean O'Malley 9.583333
John Lineker 9.516923
Gabe Green 9.316667
Fabio Maldonado 9.171970
Top ten fighters for RA_LEG:
FIGHTER RA_LEG
Marcelo Guimaraes 12.833333
Armen Petrosyan 12.400000
Joanne Wood 10.921569
Nathaniel Wood 10.174603
Joanna Jedrzejczyk 9.332143
Marcin Prachnio 9.148148
Ken Stone 8.916667
Chris Gutierrez 8.912121
Karol Rosa 8.777778
Khaos Williams 8.619048
Top ten fighters for RA_DISTANCE:
FIGHTER RA_DISTANCE
Sean O'Malley 42.091667
Daniel Rodriguez 34.331746
Max Holloway 33.709007
Emily Ducote 33.333333
Casey O'Neill 31.833333
Ian Garry 30.916667
Mounir Lazzez 30.916667
Ignacio Bahamondes 30.200000
Frank Camacho 29.750000
Pedro Munhoz 29.689963
Top ten fighters for RA_CLINCH:
FIGHTER RA_CLINCH
Josh Barnett 13.937143
Sarah Kaufman 12.944444
Lavar Johnson 9.166667
Tony Kelley 8.500000
Shavkat Rakhmonov 8.416667
Isaac Vallie-Flagg 7.916667
Marc-Andre Barriault 7.700000
Yana Kunitskaya 7.695238
Loma Lookboonmee 7.684524
Timothy Johnson 7.666667
Top ten fighters for RA_GROUND:
FIGHTER RA_GROUND
Umar Nurmagomedov 14.733333
Brock Lesnar 12.416667
Danny Henry 12.166667
Tatiana Suarez 11.907937
Tatsuro Taira 10.950000
Luke Rockhold 10.890693
Saparbeg Safarov 10.583333
Gregor Gillespie 10.108333
Mike Guymon 10.083333
Matt Grice 10.015873
Top ten fighters for RA_SIG:
FIGHTER RA_SIG
Sean O'Malley 43.975000
Casey O'Neill 38.555556
Max Holloway 38.131818
Daniel Rodriguez 35.611111
Joanne Wood 35.235294
Mounir Lazzez 34.916667
Sarah Kaufman 34.722222
Emily Ducote 34.666667
Shane Burgos 33.763636
Ian Garry 33.750000
Top ten fighters for RA_STR:
FIGHTER RA_STR
Casey O'Neill 57.111111
Lavar Johnson 55.083333
Sarah Kaufman 53.833333
Billy Quarantillo 51.314815
Pascal Krauss 50.750000
Merab Dvalishvili 50.633333
Joanne Wood 50.433155
Jared Gordon 50.136508
Karol Rosa 50.037037
Yana Kunitskaya 49.742857
Top ten fighters for RA_TD_DEF:
FIGHTER RA_TD_DEF
Mike de la Torre 3.928571
Charles Johnson 3.031746
Shane Campbell 2.822222
Petr Yan 2.793636
Diego Nunes 2.722222
Taylor Lapilus 2.711111
Adam Milstead 2.666667
Jack Della Maddalena 2.523810
Austin Lingo 2.500000
Din Thomas 2.455026
Top ten fighters for RA_HEAD_DEF:
FIGHTER RA_HEAD_DEF
Nam Phan 54.416667
Emily Ducote 48.416667
Joe Soto 42.625000
Syuri Kondo 42.361111
Nasrat Haqparast 41.716667
Vanessa Melo 41.333333
Pedro Munhoz 40.383004
Giga Chikadze 40.016667
Gloria de Paula 39.944444
Geoff Neal 39.172294
Top ten fighters for RA_BODY_DEF:
FIGHTER RA_BODY_DEF
Luana Pinheiro 5.750000
Trey Ogden 5.750000
Karolina Kowalkiewicz 5.124679
Sean Strickland 4.827302
Alptekin Ozkilic 4.694444
Manel Kape 4.666667
Nam Phan 4.458333
Randy Costa 4.333333
Ji Yeon Kim 4.233333
Daniel Zellhuber 4.138889
Top ten fighters for RA_LEG_DEF:
FIGHTER RA_LEG_DEF
Luana Pinheiro 4.055556
Hyun Gyu Lim 2.978571
Phil Harris 2.977778
Antonio Carvalho 2.750000
Karolina Kowalkiewicz 2.415705
Johnny Walker 2.366667
Andreas Michailidis 2.277778
Hannah Goldy 2.200000
Alatengheili 2.182540
Valentina Shevchenko 2.173016
Top ten fighters for RA_DISTANCE_DEF:
FIGHTER RA_DISTANCE_DEF
Nam Phan 57.750000
Emily Ducote 51.500000
Nasrat Haqparast 44.366667
Syuri Kondo 44.166667
Pedro Munhoz 43.562637
Joe Soto 42.975000
Gloria de Paula 42.777778
Giga Chikadze 42.750000
Vanessa Melo 42.666667
John Makdessi 42.027778
Top ten fighters for RA_CLINCH_DEF:
FIGHTER RA_CLINCH_DEF
Wesley Correira 5.291667
Stanislav Nedkov 5.166667
Scott Smith 4.888889
Alex Chambers 4.511111
Josh Grispi 4.305556
Andre Winner 3.944444
Jonathan Wilson 3.916667
Vik Grujic 3.833333
Rashad Coulter 3.777778
Jorge Rivera 3.677778
Top ten fighters for RA_GROUND_DEF:
FIGHTER RA_GROUND_DEF
Elvis Sinosic 11.888889
Tim Credeur 9.200000
Jason Dent 9.000000
Gadzhimurad Antigulov 8.000000
Terrion Ware 6.250000
Kyle Bradley 6.250000
Louis Gaudinot 6.111111
Jenel Lausa 5.277778
David Heath 5.033333
Aaron Phillips 4.972222
Top ten fighters for RA_SIG_DEF:
FIGHTER RA_SIG_DEF
Nam Phan 59.666667
Emily Ducote 52.083333
Syuri Kondo 46.750000
Nasrat Haqparast 44.774074
Joe Soto 44.600000
Vanessa Melo 44.416667
Pedro Munhoz 44.059780
Alptekin Ozkilic 43.472222
Giga Chikadze 43.191667
Gloria de Paula 43.111111
Top ten fighters for RA_STR_DEF:
FIGHTER RA_STR_DEF
Nam Phan 61.958333
Emily Ducote 52.500000
Syuri Kondo 49.305556
Gloria de Paula 45.972222
Vanessa Melo 45.416667
Nasrat Haqparast 45.392593
Joe Soto 45.075000
Pedro Munhoz 44.141832
Alptekin Ozkilic 43.916667
Valerie Letourneau 43.700000
Top ten fighters for RA_TD_%:
FIGHTER RA_TD_%
Johnny Walker 1.0
Sumudaerji 1.0
Jared Gooden 1.0
Uros Medic 1.0
Alex Pereira 1.0
Tom Aspinall 1.0
Andre Fialho 1.0
Omar Morales 1.0
Cheyanne Vlismas 1.0
Mariya Agapova 1.0
Top ten fighters for RA_HEAD_%:
FIGHTER RA_HEAD_%
Danilo Marques 0.754630
Brock Lesnar 0.694915
Mike Guymon 0.693467
Cyrille Diabate 0.692884
Umar Nurmagomedov 0.687320
Alistair Overeem 0.678372
Jailton Almeida 0.661527
Tatsuro Taira 0.654852
Junior Assuncao 0.632317
Darrick Minner 0.609272
Top ten fighters for RA_BODY_%:
FIGHTER RA_BODY_%
Jeremy Kimball 1.0
Jerrod Sanders 1.0
Paul Sass 1.0
Ken Stone 1.0
Shane Carwin 1.0
Chris Tuchscherer 1.0
Jake O'Brien 1.0
Alvin Robinson 1.0
Rory Singer 1.0
Marcio Cruz 1.0
Top ten fighters for RA_LEG_%:
FIGHTER RA_LEG_%
Nikolas Motta 1.0
Martin Buday 1.0
Steve Garcia 1.0
Joe Solecki 1.0
Uros Medic 1.0
Sergei Pavlovich 1.0
Tom Aspinall 1.0
Nate Maness 1.0
Jacob Malkoun 1.0
Zarah Fairn 1.0
Top ten fighters for RA_DISTANCE_%:
FIGHTER RA_DISTANCE_%
Cyrille Diabate 0.711375
Danilo Marques 0.681564
Alistair Overeem 0.655143
Tatsuro Taira 0.645985
Justin Gaethje 0.639857
Tom Aspinall 0.632530
Sean O'Malley 0.630429
Alex Pereira 0.611241
Sean Soriano 0.611033
Darrick Minner 0.599138
Top ten fighters for RA_CLINCH_%:
FIGHTER RA_CLINCH_%
Umar Nurmagomedov 1.0
Joe Pyfer 1.0
Mike Malott 1.0
Yohan Lainesse 1.0
Rafael Alves 1.0
Michael Chandler 1.0
Vanessa Melo 1.0
Chance Rencountre 1.0
Alvaro Herrera Mendoza 1.0
Dongi Yang 1.0
Top ten fighters for RA_GROUND_%:
FIGHTER RA_GROUND_%
Marcin Prachnio 1.0
Zac Pauga 1.0
Yohan Lainesse 1.0
Carlos Hernandez 1.0
Trey Ogden 1.0
Modestas Bukauskas 1.0
Hakeem Dawodu 1.0
JP Buys 1.0
Aleksa Camur 1.0
Journey Newson 1.0
Top ten fighters for RA_SIG_%:
FIGHTER RA_SIG_%
Danilo Marques 0.795222
Cyrille Diabate 0.753886
Brock Lesnar 0.752034
Mike Guymon 0.750000
Alistair Overeem 0.747861
Tatsuro Taira 0.708705
Umar Nurmagomedov 0.701841
Bartosz Fabinski 0.698113
Darrick Minner 0.687943
Josh Barnett 0.687268
Top ten fighters for RA_STR_%:
FIGHTER RA_STR_%
Mike Guymon 0.873727
Brock Lesnar 0.849720
Darrick Minner 0.805024
Bartosz Fabinski 0.803333
Magnus Cedenblad 0.801223
Aaron Phillips 0.800000
Cyrille Diabate 0.791878
Pat Sabatini 0.788863
Charlie Brenneman 0.785720
Eugene Jackson 0.782178
Top ten fighters for RA_TD_DEF_%:
FIGHTER RA_TD_DEF_%
Jailton Almeida 1.0
Philipe Lins 1.0
Umar Nurmagomedov 1.0
Yazmin Jauregui 1.0
Gregory Rodrigues 1.0
Rodolfo Vieira 1.0
Zac Pauga 1.0
Martin Buday 1.0
Shavkat Rakhmonov 1.0
Trey Ogden 1.0
Top ten fighters for RA_HEAD_DEF_%:
FIGHTER RA_HEAD_DEF_%
Jon Madsen 0.913177
Umar Nurmagomedov 0.890226
Miguel Torres 0.866083
Hector Sandoval 0.847826
Tom Aspinall 0.840000
Nate Mohr 0.830472
Claude Patrick 0.828080
Georges St-Pierre 0.816660
Montel Jackson 0.815615
Adriano Martins 0.813461
Top ten fighters for RA_BODY_DEF_%:
FIGHTER RA_BODY_DEF_%
Jason Witt 1.000000
Ivan Salaverry 0.696970
Hunter Azure 0.660920
Mike Guymon 0.642857
Phil Harris 0.627986
Anthony Birchak 0.626667
Frank Trigg 0.625000
Luana Pinheiro 0.612426
Kajan Johnson 0.612022
Nate Mohr 0.606061
Top ten fighters for RA_LEG_DEF_%:
FIGHTER RA_LEG_DEF_%
Chael Sonnen 0.616000
Jason Witt 0.600000
Jordan Leavitt 0.583784
Tatiana Suarez 0.576052
Vaughan Lee 0.566802
Gina Mazany 0.550964
Muhammad Mokaev 0.548611
Lavar Johnson 0.533333
Naoyuki Kotani 0.529412
Anthony Birchak 0.520000
Top ten fighters for RA_DISTANCE_DEF_%:
FIGHTER RA_DISTANCE_DEF_%
Jon Madsen 0.859485
Ivan Salaverry 0.839408
Claude Patrick 0.836512
Jailton Almeida 0.832512
Kevin Randleman 0.831250
Rory Singer 0.825688
Frank Trigg 0.822430
Miguel Torres 0.817469
Marcio Cruz 0.814607
Mike Guymon 0.797468
Top ten fighters for RA_CLINCH_DEF_%:
FIGHTER RA_CLINCH_DEF_%
Trey Ogden 1.000000
Ryan Jensen 0.857143
Tim Credeur 0.842105
Adrian Yanez 0.826087
Chris Dempsey 0.823529
Brian Bowles 0.804878
Mike Malott 0.750000
Greg Hardy 0.727273
Ben Nguyen 0.707317
Jason Gonzalez 0.687500
Top ten fighters for RA_GROUND_DEF_%:
FIGHTER RA_GROUND_DEF_%
Daniel Zellhuber 1.000000
Michael Morales 1.000000
Trey Ogden 1.000000
Tom Aspinall 1.000000
Rashid Magomedov 1.000000
Victoria Leonardo 0.766667
Sergey Morozov 0.750000
Darren Uyenoyama 0.727273
Fredy Serrano 0.703704
Magomed Ankalaev 0.685714
Top ten fighters for RA_SIG_DEF_%:
FIGHTER RA_SIG_DEF_%
Jon Madsen 0.820038
Umar Nurmagomedov 0.778037
Miguel Torres 0.763029
Nate Mohr 0.756364
Georges St-Pierre 0.742544
Ivan Salaverry 0.738488
Cristiane Justino 0.728064
Justin Salas 0.720828
Danielle Taylor 0.716895
Aiemann Zahabi 0.713749
Top ten fighters for RA_STR_DEF_%:
FIGHTER RA_STR_DEF_%
Jon Madsen 0.770283
Nate Mohr 0.724437
Aiemann Zahabi 0.703638
Danielle Taylor 0.700000
Justin Salas 0.693082
Cristiane Justino 0.690855
Vinny Magalhaes 0.679944
Miles Johns 0.679494
Cynthia Calvillo 0.678578
Phil Harris 0.677100
ra_champions = ra[ra['EITHER_CHAMP'] == 'Yes']
ra_champions
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | RA_FIGHTER | RA_KD | RA_TD | RA_SUB.ATT | RA_REV | RA_CTRL | RA_HEAD | RA_BODY | RA_LEG | RA_DISTANCE | RA_CLINCH | RA_GROUND | RA_SIG | RA_TOT.SIG | RA_STR | RA_TOTAL.STR | RA_TOTAL.TD | RA_TOTAL.HEAD | RA_TOTAL.BODY | RA_TOTAL.LEG | RA_TOTAL.DISTANCE | RA_TOTAL.CLINCH | RA_TOTAL.GROUND | RA_OPP_KD | RA_OPP_TD | RA_OPP_SUB.ATT | RA_OPP_REV | RA_OPP_CTRL | RA_OPP_HEAD | RA_OPP_BODY | RA_OPP_LEG | RA_OPP_DISTANCE | RA_OPP_CLINCH | RA_OPP_GROUND | RA_OPP_SIG | RA_OPP_TOT.SIG | RA_OPP_STR | RA_OPP_TOTAL.STR | RA_OPP_TOTAL.TD | RA_OPP_TOTAL.HEAD | RA_OPP_TOTAL.BODY | RA_OPP_TOTAL.LEG | RA_OPP_TOTAL.DISTANCE | RA_OPP_TOTAL.CLINCH | RA_OPP_TOTAL.GROUND | RA_TD_DEF | RA_HEAD_DEF | RA_BODY_DEF | RA_LEG_DEF | RA_DISTANCE_DEF | RA_CLINCH_DEF | RA_GROUND_DEF | RA_SIG_DEF | RA_STR_DEF | RA_TD_% | RA_HEAD_% | RA_BODY_% | RA_LEG_% | RA_DISTANCE_% | RA_CLINCH_% | RA_GROUND_% | RA_SIG_% | RA_STR_% | RA_TD_DEF_% | RA_HEAD_DEF_% | RA_BODY_DEF_% | RA_LEG_DEF_% | RA_DISTANCE_DEF_% | RA_CLINCH_DEF_% | RA_GROUND_DEF_% | RA_SIG_DEF_% | RA_STR_DEF_% | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5 | Dustin Poirier | Southpaw | No | Yes | Yes | Yes | Dustin Poirier | 0.104762 | 0.357143 | 0.340476 | 0.032381 | 0.937897 | 19.855238 | 2.094762 | 1.622857 | 18.732857 | 2.650000 | 2.190000 | 23.572857 | 43.810476 | 29.180000 | 49.939524 | 0.991429 | 39.138095 | 2.737143 | 1.935238 | 36.829524 | 3.870476 | 3.110476 | 0.022857 | 0.444762 | 0.381905 | 0.025714 | 0.798706 | 12.851905 | 4.429524 | 1.852381 | 15.667143 | 2.081905 | 1.384762 | 19.133810 | 41.794286 | 25.088571 | 48.283810 | 1.320952 | 33.875238 | 5.760952 | 2.158095 | 37.068571 | 3.022857 | 1.702857 | 0.876190 | 21.023333 | 1.331429 | 0.305714 | 21.401429 | 0.940952 | 0.318095 | 22.660476 | 23.195238 | 0.360231 | 0.507312 | 0.765310 | 0.838583 | 0.508637 | 0.684670 | 0.704072 | 0.538064 | 0.584307 | 0.663302 | 0.620611 | 0.231113 | 0.141659 | 0.577347 | 0.311279 | 0.186801 | 0.542191 | 0.480394 |
| 17 | Rafael Dos Anjos | Southpaw | Yes | No | Yes | Yes | Rafael Dos Anjos | 0.043160 | 0.619217 | 0.140167 | 0.020303 | 1.268712 | 10.169100 | 3.897003 | 2.866448 | 11.633615 | 3.032440 | 2.266496 | 16.932551 | 34.930426 | 23.730590 | 42.639434 | 1.778249 | 26.360145 | 5.202845 | 3.367436 | 27.883052 | 3.740788 | 3.306586 | 0.027692 | 0.681438 | 0.013407 | 0.012611 | 1.146093 | 11.184345 | 3.144810 | 2.036012 | 13.011384 | 2.032737 | 1.321045 | 16.365167 | 39.042653 | 24.002827 | 47.722363 | 1.535812 | 32.130460 | 4.677119 | 2.235073 | 34.267488 | 2.747988 | 2.027177 | 0.854375 | 20.946115 | 1.532310 | 0.199062 | 21.256103 | 0.715251 | 0.706132 | 22.677486 | 23.719536 | 0.348217 | 0.385776 | 0.749014 | 0.851226 | 0.417229 | 0.810642 | 0.685449 | 0.484751 | 0.556541 | 0.556301 | 0.651908 | 0.327618 | 0.089063 | 0.620299 | 0.260282 | 0.348333 | 0.580839 | 0.497032 |
| 22 | Petr Yan | Switch | Yes | Yes | Yes | Yes | Petr Yan | 0.197902 | 0.470559 | 0.056667 | 0.034848 | 0.879949 | 18.562191 | 4.925058 | 2.606597 | 17.944266 | 2.520023 | 5.629557 | 26.093846 | 46.200629 | 34.872984 | 55.728811 | 1.014918 | 37.513450 | 5.920093 | 2.767086 | 36.311189 | 3.238228 | 6.651212 | 0.016667 | 0.529417 | 0.000000 | 0.033566 | 0.557225 | 13.148485 | 4.963683 | 2.781538 | 18.731608 | 1.597599 | 0.564499 | 20.893706 | 49.988741 | 24.471026 | 54.071795 | 3.323054 | 39.285245 | 7.205921 | 3.497576 | 46.977413 | 2.244009 | 0.767319 | 2.793636 | 26.136760 | 2.242238 | 0.716037 | 28.245804 | 0.646410 | 0.202821 | 29.095035 | 29.600769 | 0.463643 | 0.494814 | 0.831922 | 0.942001 | 0.494180 | 0.778210 | 0.846396 | 0.564794 | 0.625762 | 0.840684 | 0.665307 | 0.311166 | 0.204724 | 0.601264 | 0.288060 | 0.264323 | 0.582032 | 0.547435 |
| 27 | Sean O'Malley | Switch | Yes | No | Yes | Yes | Sean O'Malley | 0.125000 | 0.100000 | 0.075000 | 0.025000 | 0.052639 | 31.758333 | 9.583333 | 2.633333 | 42.091667 | 1.041667 | 0.841667 | 43.975000 | 69.083333 | 45.058333 | 70.333333 | 0.183333 | 55.041667 | 11.333333 | 2.708333 | 66.766667 | 1.316667 | 1.000000 | 0.000000 | 0.300000 | 0.000000 | 0.000000 | 0.384583 | 9.366667 | 3.375000 | 5.816667 | 16.850000 | 1.125000 | 0.583333 | 18.558333 | 48.775000 | 20.325000 | 50.775000 | 0.741667 | 35.225000 | 6.666667 | 6.883333 | 46.241667 | 1.741667 | 0.791667 | 0.441667 | 25.858333 | 3.291667 | 1.066667 | 29.391667 | 0.616667 | 0.208333 | 30.216667 | 30.450000 | 0.545455 | 0.576987 | 0.845588 | 0.972308 | 0.630429 | 0.791139 | 0.841667 | 0.636550 | 0.640640 | 0.595506 | 0.734090 | 0.493750 | 0.154964 | 0.635610 | 0.354067 | 0.263158 | 0.619511 | 0.599705 |
| 50 | Brandon Moreno | Orthodox | Yes | Yes | Yes | Yes | Brandon Moreno | 0.056456 | 0.544780 | 0.107555 | 0.110055 | 0.923321 | 14.478764 | 3.475577 | 1.919121 | 17.296896 | 1.217363 | 1.359203 | 19.873462 | 44.118269 | 24.699313 | 49.308874 | 1.050852 | 37.010330 | 4.804505 | 2.303434 | 39.869478 | 2.098791 | 2.150000 | 0.097555 | 0.491923 | 0.039286 | 0.040385 | 0.585583 | 13.879670 | 3.679396 | 2.347363 | 17.844011 | 0.779780 | 1.282637 | 19.906429 | 50.026868 | 24.637225 | 55.806346 | 1.227802 | 41.806346 | 4.888297 | 3.332225 | 47.007555 | 1.345055 | 1.674258 | 0.735879 | 27.926676 | 1.208901 | 0.984863 | 29.163544 | 0.565275 | 0.391621 | 30.120440 | 31.169121 | 0.518418 | 0.391209 | 0.723400 | 0.833156 | 0.433838 | 0.580030 | 0.632188 | 0.450459 | 0.500910 | 0.599347 | 0.668001 | 0.247305 | 0.295557 | 0.620401 | 0.420261 | 0.233907 | 0.602085 | 0.558523 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2165 | Dave Menne | Orthodox | Yes | No | Yes | Yes | Dave Menne | 0.000000 | 0.080000 | 0.853333 | 0.233333 | 0.853000 | 6.166667 | 1.760000 | 3.320000 | 4.606667 | 4.573333 | 2.066667 | 11.246667 | 22.093333 | 24.086667 | 35.566667 | 0.160000 | 15.620000 | 1.906667 | 4.566667 | 12.906667 | 6.600000 | 2.586667 | 0.146667 | 1.160000 | 0.040000 | 0.040000 | 2.447444 | 3.920000 | 1.653333 | 0.360000 | 2.413333 | 0.720000 | 2.800000 | 5.933333 | 11.200000 | 21.773333 | 27.826667 | 3.046667 | 8.840000 | 1.960000 | 0.400000 | 6.773333 | 1.020000 | 3.406667 | 1.886667 | 4.920000 | 0.306667 | 0.040000 | 4.360000 | 0.300000 | 0.606667 | 5.266667 | 6.053333 | 0.500000 | 0.394793 | 0.923077 | 0.727007 | 0.356921 | 0.692929 | 0.798969 | 0.509053 | 0.677226 | 0.619256 | 0.556561 | 0.156463 | 0.100000 | 0.643701 | 0.294118 | 0.178082 | 0.470238 | 0.217537 |
| 2241 | Carlos Newton | Orthodox | Yes | No | Yes | Yes | Carlos Newton | 0.000000 | 0.441667 | 0.245833 | 0.183333 | 0.732361 | 0.745833 | 0.508333 | 0.183333 | 0.687500 | 0.520833 | 0.229167 | 1.437500 | 4.862500 | 5.912500 | 9.775000 | 0.787500 | 4.108333 | 0.570833 | 0.183333 | 3.491667 | 0.829167 | 0.541667 | 0.000000 | 0.912500 | 0.183333 | 0.050000 | 2.566806 | 7.733333 | 1.383333 | 0.575000 | 2.045833 | 0.491667 | 7.154167 | 9.691667 | 16.154167 | 24.345833 | 31.908333 | 1.120833 | 14.145833 | 1.433333 | 0.575000 | 5.500000 | 0.641667 | 10.012500 | 0.208333 | 6.412500 | 0.050000 | 0.000000 | 3.454167 | 0.150000 | 2.858333 | 6.462500 | 7.562500 | 0.560847 | 0.181542 | 0.890511 | 1.000000 | 0.196897 | 0.628141 | 0.423077 | 0.295630 | 0.604859 | 0.185874 | 0.453314 | 0.034884 | 0.000000 | 0.628030 | 0.233766 | 0.285476 | 0.400052 | 0.237007 |
| 2246 | Ricco Rodriguez | Orthodox | Yes | No | Yes | Yes | Ricco Rodriguez | 0.000000 | 0.907143 | 0.192857 | 0.061905 | 2.530516 | 9.514286 | 1.047619 | 0.366667 | 1.554762 | 0.859524 | 8.514286 | 10.928571 | 19.852381 | 30.600000 | 40.557143 | 1.745238 | 18.121429 | 1.330952 | 0.400000 | 4.909524 | 1.107143 | 13.835714 | 0.028571 | 0.264286 | 0.028571 | 0.050000 | 0.776270 | 3.669048 | 0.442857 | 1.004762 | 1.704762 | 0.861905 | 2.550000 | 5.116667 | 11.421429 | 10.069048 | 16.559524 | 0.442857 | 9.704762 | 0.504762 | 1.211905 | 5.961905 | 1.483333 | 3.976190 | 0.178571 | 6.035714 | 0.061905 | 0.207143 | 4.257143 | 0.621429 | 1.426190 | 6.304762 | 6.490476 | 0.519782 | 0.525030 | 0.787120 | 0.916667 | 0.316683 | 0.776344 | 0.615385 | 0.550492 | 0.754491 | 0.403226 | 0.621933 | 0.122642 | 0.170923 | 0.714058 | 0.418941 | 0.358683 | 0.552012 | 0.391948 |
| 2278 | Pat Miletich | Orthodox | Yes | No | Yes | Yes | Pat Miletich | 0.083333 | 0.433333 | 0.233333 | 0.000000 | 0.985556 | 4.483333 | 0.683333 | 1.600000 | 4.766667 | 0.950000 | 1.050000 | 6.766667 | 13.150000 | 17.816667 | 24.683333 | 0.433333 | 10.650000 | 0.766667 | 1.733333 | 10.483333 | 1.316667 | 1.350000 | 0.000000 | 0.633333 | 0.483333 | 0.000000 | 0.905000 | 2.133333 | 1.050000 | 1.066667 | 2.066667 | 0.766667 | 1.416667 | 4.250000 | 12.216667 | 12.550000 | 21.716667 | 1.066667 | 9.783333 | 1.216667 | 1.216667 | 7.933333 | 1.250000 | 3.033333 | 0.433333 | 7.650000 | 0.166667 | 0.150000 | 5.866667 | 0.483333 | 1.616667 | 7.966667 | 9.166667 | 1.000000 | 0.420970 | 0.891304 | 0.923077 | 0.454690 | 0.721519 | 0.777778 | 0.514575 | 0.721810 | 0.406250 | 0.781942 | 0.136986 | 0.123288 | 0.739496 | 0.386667 | 0.532967 | 0.652115 | 0.422103 |
| 2282 | Kevin Randleman | Orthodox | Yes | No | Yes | Yes | Kevin Randleman | 0.000000 | 0.283333 | 0.150000 | 0.000000 | 1.512778 | 3.600000 | 0.333333 | 0.066667 | 1.566667 | 0.783333 | 1.650000 | 4.000000 | 9.250000 | 10.850000 | 16.666667 | 0.500000 | 8.850000 | 0.333333 | 0.066667 | 5.016667 | 1.050000 | 3.183333 | 0.050000 | 0.066667 | 0.383333 | 0.000000 | 0.198333 | 1.500000 | 0.583333 | 0.850000 | 0.900000 | 1.033333 | 1.000000 | 2.933333 | 8.016667 | 7.366667 | 12.683333 | 0.066667 | 6.383333 | 0.650000 | 0.983333 | 5.333333 | 1.233333 | 1.450000 | 0.000000 | 4.883333 | 0.066667 | 0.133333 | 4.433333 | 0.200000 | 0.450000 | 5.083333 | 5.316667 | 0.566667 | 0.406780 | 1.000000 | 1.000000 | 0.312292 | 0.746032 | 0.518325 | 0.432432 | 0.651000 | 0.000000 | 0.765013 | 0.102564 | 0.135593 | 0.831250 | 0.162162 | 0.310345 | 0.634096 | 0.419185 |
103 rows × 78 columns
# Calculate the mean for each of the specified columns
means1 = ra_champions[metrics1].mean()
# Rank the means from highest to lowest and plot again
sorted_means1 = means1.sort_values(ascending=False)
# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_means1.plot(kind='bar', color='palegreen')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90) # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()
# Calculate the mean for each of the specified columns
meansA = ra_champions[metrics2].mean()
# Rank the means from highest to lowest and plot again
sorted_meansA = meansA.sort_values(ascending=False)
# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_meansA.plot(kind='bar', color='hotpink')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)
plt.show()
ra_challengers = ra[ra['EVER_CHALLENGER'] == 'Yes']
ra_challengers
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | RA_FIGHTER | RA_KD | RA_TD | RA_SUB.ATT | RA_REV | RA_CTRL | RA_HEAD | RA_BODY | RA_LEG | RA_DISTANCE | RA_CLINCH | RA_GROUND | RA_SIG | RA_TOT.SIG | RA_STR | RA_TOTAL.STR | RA_TOTAL.TD | RA_TOTAL.HEAD | RA_TOTAL.BODY | RA_TOTAL.LEG | RA_TOTAL.DISTANCE | RA_TOTAL.CLINCH | RA_TOTAL.GROUND | RA_OPP_KD | RA_OPP_TD | RA_OPP_SUB.ATT | RA_OPP_REV | RA_OPP_CTRL | RA_OPP_HEAD | RA_OPP_BODY | RA_OPP_LEG | RA_OPP_DISTANCE | RA_OPP_CLINCH | RA_OPP_GROUND | RA_OPP_SIG | RA_OPP_TOT.SIG | RA_OPP_STR | RA_OPP_TOTAL.STR | RA_OPP_TOTAL.TD | RA_OPP_TOTAL.HEAD | RA_OPP_TOTAL.BODY | RA_OPP_TOTAL.LEG | RA_OPP_TOTAL.DISTANCE | RA_OPP_TOTAL.CLINCH | RA_OPP_TOTAL.GROUND | RA_TD_DEF | RA_HEAD_DEF | RA_BODY_DEF | RA_LEG_DEF | RA_DISTANCE_DEF | RA_CLINCH_DEF | RA_GROUND_DEF | RA_SIG_DEF | RA_STR_DEF | RA_TD_% | RA_HEAD_% | RA_BODY_% | RA_LEG_% | RA_DISTANCE_% | RA_CLINCH_% | RA_GROUND_% | RA_SIG_% | RA_STR_% | RA_TD_DEF_% | RA_HEAD_DEF_% | RA_BODY_DEF_% | RA_LEG_DEF_% | RA_DISTANCE_DEF_% | RA_CLINCH_DEF_% | RA_GROUND_DEF_% | RA_SIG_DEF_% | RA_STR_DEF_% | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5 | Dustin Poirier | Southpaw | No | Yes | Yes | Yes | Dustin Poirier | 0.104762 | 0.357143 | 0.340476 | 0.032381 | 0.937897 | 19.855238 | 2.094762 | 1.622857 | 18.732857 | 2.650000 | 2.190000 | 23.572857 | 43.810476 | 29.180000 | 49.939524 | 0.991429 | 39.138095 | 2.737143 | 1.935238 | 36.829524 | 3.870476 | 3.110476 | 0.022857 | 0.444762 | 0.381905 | 0.025714 | 0.798706 | 12.851905 | 4.429524 | 1.852381 | 15.667143 | 2.081905 | 1.384762 | 19.133810 | 41.794286 | 25.088571 | 48.283810 | 1.320952 | 33.875238 | 5.760952 | 2.158095 | 37.068571 | 3.022857 | 1.702857 | 0.876190 | 21.023333 | 1.331429 | 0.305714 | 21.401429 | 0.940952 | 0.318095 | 22.660476 | 23.195238 | 0.360231 | 0.507312 | 0.765310 | 0.838583 | 0.508637 | 0.684670 | 0.704072 | 0.538064 | 0.584307 | 0.663302 | 0.620611 | 0.231113 | 0.141659 | 0.577347 | 0.311279 | 0.186801 | 0.542191 | 0.480394 |
| 6 | Gilbert Burns | Orthodox | No | No | Yes | No | Gilbert Burns | 0.041711 | 0.530710 | 0.188464 | 0.000000 | 1.051308 | 8.479374 | 3.693736 | 3.561956 | 12.524293 | 1.535371 | 1.675401 | 15.735065 | 30.133766 | 24.078151 | 39.108938 | 1.951566 | 21.409015 | 4.409702 | 4.315050 | 26.107945 | 1.971811 | 2.054011 | 0.093659 | 0.130176 | 0.009091 | 0.000000 | 0.492265 | 11.871429 | 4.073415 | 1.743392 | 15.548358 | 1.317189 | 0.822689 | 17.688235 | 36.805806 | 22.144003 | 41.636440 | 0.353934 | 29.493736 | 5.339801 | 1.972269 | 34.036440 | 1.608785 | 1.160581 | 0.223759 | 17.622307 | 1.266387 | 0.228877 | 18.488083 | 0.291597 | 0.337892 | 19.117571 | 19.492437 | 0.271941 | 0.396066 | 0.837638 | 0.825473 | 0.479712 | 0.778660 | 0.815673 | 0.522174 | 0.615669 | 0.632204 | 0.597493 | 0.237160 | 0.116048 | 0.543185 | 0.181253 | 0.291140 | 0.519417 | 0.468158 |
| 17 | Rafael Dos Anjos | Southpaw | Yes | No | Yes | Yes | Rafael Dos Anjos | 0.043160 | 0.619217 | 0.140167 | 0.020303 | 1.268712 | 10.169100 | 3.897003 | 2.866448 | 11.633615 | 3.032440 | 2.266496 | 16.932551 | 34.930426 | 23.730590 | 42.639434 | 1.778249 | 26.360145 | 5.202845 | 3.367436 | 27.883052 | 3.740788 | 3.306586 | 0.027692 | 0.681438 | 0.013407 | 0.012611 | 1.146093 | 11.184345 | 3.144810 | 2.036012 | 13.011384 | 2.032737 | 1.321045 | 16.365167 | 39.042653 | 24.002827 | 47.722363 | 1.535812 | 32.130460 | 4.677119 | 2.235073 | 34.267488 | 2.747988 | 2.027177 | 0.854375 | 20.946115 | 1.532310 | 0.199062 | 21.256103 | 0.715251 | 0.706132 | 22.677486 | 23.719536 | 0.348217 | 0.385776 | 0.749014 | 0.851226 | 0.417229 | 0.810642 | 0.685449 | 0.484751 | 0.556541 | 0.556301 | 0.651908 | 0.327618 | 0.089063 | 0.620299 | 0.260282 | 0.348333 | 0.580839 | 0.497032 |
| 22 | Petr Yan | Switch | Yes | Yes | Yes | Yes | Petr Yan | 0.197902 | 0.470559 | 0.056667 | 0.034848 | 0.879949 | 18.562191 | 4.925058 | 2.606597 | 17.944266 | 2.520023 | 5.629557 | 26.093846 | 46.200629 | 34.872984 | 55.728811 | 1.014918 | 37.513450 | 5.920093 | 2.767086 | 36.311189 | 3.238228 | 6.651212 | 0.016667 | 0.529417 | 0.000000 | 0.033566 | 0.557225 | 13.148485 | 4.963683 | 2.781538 | 18.731608 | 1.597599 | 0.564499 | 20.893706 | 49.988741 | 24.471026 | 54.071795 | 3.323054 | 39.285245 | 7.205921 | 3.497576 | 46.977413 | 2.244009 | 0.767319 | 2.793636 | 26.136760 | 2.242238 | 0.716037 | 28.245804 | 0.646410 | 0.202821 | 29.095035 | 29.600769 | 0.463643 | 0.494814 | 0.831922 | 0.942001 | 0.494180 | 0.778210 | 0.846396 | 0.564794 | 0.625762 | 0.840684 | 0.665307 | 0.311166 | 0.204724 | 0.601264 | 0.288060 | 0.264323 | 0.582032 | 0.547435 |
| 26 | Marlon Vera | Switch | No | No | Yes | No | Marlon Vera | 0.247391 | 0.110978 | 0.182174 | 0.010000 | 0.374172 | 12.118986 | 4.203841 | 4.964203 | 17.284457 | 2.013116 | 1.989457 | 21.287029 | 43.797717 | 23.702428 | 46.697862 | 0.278152 | 31.006522 | 6.923406 | 5.867790 | 38.388804 | 2.434964 | 2.973949 | 0.000000 | 0.267319 | 0.000000 | 0.018696 | 0.705589 | 20.315145 | 6.326884 | 3.546920 | 26.958478 | 1.659275 | 1.571196 | 30.188949 | 59.976993 | 34.725652 | 65.684457 | 1.119928 | 47.529638 | 8.321739 | 4.125616 | 55.435688 | 2.302681 | 2.238623 | 0.852609 | 27.214493 | 1.994855 | 0.578696 | 28.477210 | 0.643406 | 0.667428 | 29.788043 | 30.958804 | 0.398984 | 0.390853 | 0.607193 | 0.846009 | 0.450247 | 0.826754 | 0.668961 | 0.486031 | 0.507570 | 0.761307 | 0.572579 | 0.239716 | 0.140269 | 0.513698 | 0.279416 | 0.298142 | 0.496658 | 0.471326 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2245 | Pedro Rizzo | Orthodox | No | No | Yes | No | Pedro Rizzo | 0.103333 | 0.045000 | 0.000000 | 0.000000 | 0.546778 | 3.351667 | 0.490000 | 2.723333 | 4.548333 | 0.935000 | 1.081667 | 6.565000 | 19.076667 | 10.651667 | 23.543333 | 0.163333 | 14.890000 | 0.490000 | 3.696667 | 15.856667 | 1.390000 | 1.830000 | 0.020000 | 0.408333 | 0.040000 | 0.000000 | 0.769444 | 5.956667 | 0.595000 | 2.345000 | 5.720000 | 1.935000 | 1.241667 | 8.896667 | 19.263333 | 16.455000 | 27.180000 | 1.963333 | 15.356667 | 0.823333 | 3.083333 | 14.441667 | 2.881667 | 1.940000 | 1.555000 | 9.400000 | 0.228333 | 0.738333 | 8.721667 | 0.946667 | 0.698333 | 10.366667 | 10.725000 | 0.275510 | 0.225095 | 1.000000 | 0.736700 | 0.286840 | 0.672662 | 0.591075 | 0.344138 | 0.452428 | 0.792020 | 0.612112 | 0.277328 | 0.239459 | 0.603924 | 0.328514 | 0.359966 | 0.538155 | 0.394592 |
| 2246 | Ricco Rodriguez | Orthodox | Yes | No | Yes | Yes | Ricco Rodriguez | 0.000000 | 0.907143 | 0.192857 | 0.061905 | 2.530516 | 9.514286 | 1.047619 | 0.366667 | 1.554762 | 0.859524 | 8.514286 | 10.928571 | 19.852381 | 30.600000 | 40.557143 | 1.745238 | 18.121429 | 1.330952 | 0.400000 | 4.909524 | 1.107143 | 13.835714 | 0.028571 | 0.264286 | 0.028571 | 0.050000 | 0.776270 | 3.669048 | 0.442857 | 1.004762 | 1.704762 | 0.861905 | 2.550000 | 5.116667 | 11.421429 | 10.069048 | 16.559524 | 0.442857 | 9.704762 | 0.504762 | 1.211905 | 5.961905 | 1.483333 | 3.976190 | 0.178571 | 6.035714 | 0.061905 | 0.207143 | 4.257143 | 0.621429 | 1.426190 | 6.304762 | 6.490476 | 0.519782 | 0.525030 | 0.787120 | 0.916667 | 0.316683 | 0.776344 | 0.615385 | 0.550492 | 0.754491 | 0.403226 | 0.621933 | 0.122642 | 0.170923 | 0.714058 | 0.418941 | 0.358683 | 0.552012 | 0.391948 |
| 2251 | Gan McGee | Orthodox | No | No | Yes | No | Gan McGee | 0.000000 | 0.625000 | 0.000000 | 0.000000 | 2.116667 | 5.250000 | 0.500000 | 0.750000 | 3.625000 | 1.000000 | 1.875000 | 6.500000 | 17.375000 | 8.750000 | 19.750000 | 1.750000 | 16.000000 | 0.500000 | 0.875000 | 12.500000 | 1.125000 | 3.750000 | 0.125000 | 0.500000 | 0.625000 | 0.000000 | 0.900000 | 6.750000 | 2.125000 | 2.375000 | 3.000000 | 2.750000 | 5.500000 | 11.250000 | 19.875000 | 22.500000 | 32.375000 | 1.375000 | 15.375000 | 2.125000 | 2.375000 | 9.500000 | 3.250000 | 7.125000 | 0.875000 | 8.625000 | 0.000000 | 0.000000 | 6.500000 | 0.500000 | 1.625000 | 8.625000 | 9.875000 | 0.357143 | 0.328125 | 1.000000 | 0.857143 | 0.290000 | 0.888889 | 0.500000 | 0.374101 | 0.443038 | 0.636364 | 0.560976 | 0.000000 | 0.000000 | 0.684211 | 0.153846 | 0.228070 | 0.433962 | 0.305019 |
| 2278 | Pat Miletich | Orthodox | Yes | No | Yes | Yes | Pat Miletich | 0.083333 | 0.433333 | 0.233333 | 0.000000 | 0.985556 | 4.483333 | 0.683333 | 1.600000 | 4.766667 | 0.950000 | 1.050000 | 6.766667 | 13.150000 | 17.816667 | 24.683333 | 0.433333 | 10.650000 | 0.766667 | 1.733333 | 10.483333 | 1.316667 | 1.350000 | 0.000000 | 0.633333 | 0.483333 | 0.000000 | 0.905000 | 2.133333 | 1.050000 | 1.066667 | 2.066667 | 0.766667 | 1.416667 | 4.250000 | 12.216667 | 12.550000 | 21.716667 | 1.066667 | 9.783333 | 1.216667 | 1.216667 | 7.933333 | 1.250000 | 3.033333 | 0.433333 | 7.650000 | 0.166667 | 0.150000 | 5.866667 | 0.483333 | 1.616667 | 7.966667 | 9.166667 | 1.000000 | 0.420970 | 0.891304 | 0.923077 | 0.454690 | 0.721519 | 0.777778 | 0.514575 | 0.721810 | 0.406250 | 0.781942 | 0.136986 | 0.123288 | 0.739496 | 0.386667 | 0.532967 | 0.652115 | 0.422103 |
| 2282 | Kevin Randleman | Orthodox | Yes | No | Yes | Yes | Kevin Randleman | 0.000000 | 0.283333 | 0.150000 | 0.000000 | 1.512778 | 3.600000 | 0.333333 | 0.066667 | 1.566667 | 0.783333 | 1.650000 | 4.000000 | 9.250000 | 10.850000 | 16.666667 | 0.500000 | 8.850000 | 0.333333 | 0.066667 | 5.016667 | 1.050000 | 3.183333 | 0.050000 | 0.066667 | 0.383333 | 0.000000 | 0.198333 | 1.500000 | 0.583333 | 0.850000 | 0.900000 | 1.033333 | 1.000000 | 2.933333 | 8.016667 | 7.366667 | 12.683333 | 0.066667 | 6.383333 | 0.650000 | 0.983333 | 5.333333 | 1.233333 | 1.450000 | 0.000000 | 4.883333 | 0.066667 | 0.133333 | 4.433333 | 0.200000 | 0.450000 | 5.083333 | 5.316667 | 0.566667 | 0.406780 | 1.000000 | 1.000000 | 0.312292 | 0.746032 | 0.518325 | 0.432432 | 0.651000 | 0.000000 | 0.765013 | 0.102564 | 0.135593 | 0.831250 | 0.162162 | 0.310345 | 0.634096 | 0.419185 |
213 rows × 78 columns
# Calculate the mean for each of the specified columns
means2 = ra_challengers[metrics1].mean()
# Rank the means from highest to lowest and plot again
sorted_means2 = means2.sort_values(ascending=False)
# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_means2.plot(kind='bar', color='goldenrod')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90) # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()
# Calculate the mean for each of the specified columns
meansB = ra_challengers[metrics2].mean()
# Rank the means from highest to lowest and plot again
sorted_meansB = meansB.sort_values(ascending=False)
# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_meansB.plot(kind='bar', color='deeppink')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)
plt.show()
ra_non_champions = ra[ra['EITHER_CHAMP'] != 'Yes']
ra_non_champions
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | RA_FIGHTER | RA_KD | RA_TD | RA_SUB.ATT | RA_REV | RA_CTRL | RA_HEAD | RA_BODY | RA_LEG | RA_DISTANCE | RA_CLINCH | RA_GROUND | RA_SIG | RA_TOT.SIG | RA_STR | RA_TOTAL.STR | RA_TOTAL.TD | RA_TOTAL.HEAD | RA_TOTAL.BODY | RA_TOTAL.LEG | RA_TOTAL.DISTANCE | RA_TOTAL.CLINCH | RA_TOTAL.GROUND | RA_OPP_KD | RA_OPP_TD | RA_OPP_SUB.ATT | RA_OPP_REV | RA_OPP_CTRL | RA_OPP_HEAD | RA_OPP_BODY | RA_OPP_LEG | RA_OPP_DISTANCE | RA_OPP_CLINCH | RA_OPP_GROUND | RA_OPP_SIG | RA_OPP_TOT.SIG | RA_OPP_STR | RA_OPP_TOTAL.STR | RA_OPP_TOTAL.TD | RA_OPP_TOTAL.HEAD | RA_OPP_TOTAL.BODY | RA_OPP_TOTAL.LEG | RA_OPP_TOTAL.DISTANCE | RA_OPP_TOTAL.CLINCH | RA_OPP_TOTAL.GROUND | RA_TD_DEF | RA_HEAD_DEF | RA_BODY_DEF | RA_LEG_DEF | RA_DISTANCE_DEF | RA_CLINCH_DEF | RA_GROUND_DEF | RA_SIG_DEF | RA_STR_DEF | RA_TD_% | RA_HEAD_% | RA_BODY_% | RA_LEG_% | RA_DISTANCE_% | RA_CLINCH_% | RA_GROUND_% | RA_SIG_% | RA_STR_% | RA_TD_DEF_% | RA_HEAD_DEF_% | RA_BODY_DEF_% | RA_LEG_DEF_% | RA_DISTANCE_DEF_% | RA_CLINCH_DEF_% | RA_GROUND_DEF_% | RA_SIG_DEF_% | RA_STR_DEF_% | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | CJ Vergara | Orthodox | No | No | No | No | CJ Vergara | 0.000000 | 0.000000 | 0.055556 | 0.055556 | 0.681481 | 12.750000 | 9.861111 | 3.083333 | 18.250000 | 2.500000 | 4.944444 | 25.694444 | 44.611111 | 37.111111 | 57.305556 | 0.000000 | 28.472222 | 12.638889 | 3.500000 | 34.111111 | 3.250000 | 7.250000 | 0.111111 | 1.000000 | 0.305556 | 0.138889 | 1.258796 | 14.000000 | 5.777778 | 2.916667 | 19.722222 | 1.527778 | 1.444444 | 22.694444 | 48.111111 | 27.361111 | 54.027778 | 2.555556 | 37.333333 | 7.638889 | 3.138889 | 44.250000 | 2.027778 | 1.833333 | 1.555556 | 23.333333 | 1.861111 | 0.222222 | 24.527778 | 0.500000 | 0.388889 | 25.416667 | 26.666667 | NaN | 0.447805 | 0.780220 | 0.880952 | 0.535016 | 0.769231 | 0.681992 | 0.575965 | 0.647601 | 0.608696 | 0.625000 | 0.243636 | 0.070796 | 0.554300 | 0.246575 | 0.212121 | 0.528291 | 0.493573 |
| 2 | Curtis Blaydes | Orthodox | No | No | No | No | Curtis Blaydes | 0.024444 | 1.886667 | 0.000000 | 0.000000 | 2.670889 | 8.462222 | 1.548889 | 1.500000 | 5.433333 | 1.900000 | 4.177778 | 11.511111 | 21.655556 | 22.500000 | 35.106667 | 3.837778 | 18.253333 | 1.726667 | 1.675556 | 14.168889 | 2.197778 | 5.288889 | 0.046667 | 0.333333 | 0.026667 | 0.000000 | 0.216815 | 5.788889 | 1.411111 | 0.400000 | 6.882222 | 0.542222 | 0.175556 | 7.600000 | 19.677778 | 26.988889 | 40.008889 | 0.613333 | 17.313333 | 1.906667 | 0.457778 | 18.555556 | 0.902222 | 0.220000 | 0.280000 | 11.524444 | 0.495556 | 0.057778 | 11.673333 | 0.360000 | 0.044444 | 12.077778 | 13.020000 | 0.491604 | 0.463599 | 0.897040 | 0.895225 | 0.383469 | 0.864510 | 0.789916 | 0.531555 | 0.640904 | 0.456522 | 0.665640 | 0.259907 | 0.126214 | 0.629102 | 0.399015 | 0.202020 | 0.613778 | 0.325428 |
| 3 | Jailton Almeida | Orthodox | No | No | No | No | Jailton Almeida | 0.000000 | 1.476190 | 0.304762 | 0.200000 | 3.737937 | 8.580952 | 0.571429 | 0.266667 | 0.647619 | 0.600000 | 8.171429 | 9.419048 | 14.228571 | 27.542857 | 35.980952 | 3.409524 | 12.971429 | 0.628571 | 0.628571 | 1.409524 | 1.600000 | 11.219048 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.198730 | 3.676190 | 0.257143 | 0.028571 | 0.323810 | 0.200000 | 3.438095 | 3.961905 | 7.352381 | 5.923810 | 10.219048 | 0.400000 | 6.809524 | 0.514286 | 0.028571 | 1.933333 | 0.200000 | 5.219048 | 0.400000 | 3.133333 | 0.257143 | 0.000000 | 1.609524 | 0.000000 | 1.780952 | 3.390476 | 4.295238 | 0.432961 | 0.661527 | 0.909091 | 0.424242 | 0.459459 | 0.375000 | 0.728353 | 0.661981 | 0.765484 | 1.000000 | 0.460140 | 0.500000 | 0.000000 | 0.832512 | 0.000000 | 0.341241 | 0.461140 | 0.420317 |
| 4 | Benoit Saint Denis | Southpaw | No | No | No | No | Benoit Saint Denis | 0.209524 | 0.895238 | 0.257143 | 0.047619 | 1.433492 | 11.952381 | 6.561905 | 2.961905 | 14.514286 | 2.761905 | 4.200000 | 21.476190 | 39.304762 | 29.761905 | 49.047619 | 3.780952 | 28.190476 | 8.057143 | 3.057143 | 28.009524 | 3.504762 | 7.790476 | 0.066667 | 0.209524 | 0.323810 | 0.000000 | 0.547302 | 11.952381 | 6.828571 | 2.657143 | 16.561905 | 3.580952 | 1.295238 | 21.438095 | 38.009524 | 25.419048 | 42.790476 | 0.676190 | 27.085714 | 7.885714 | 3.038095 | 31.771429 | 4.942857 | 1.295238 | 0.466667 | 15.133333 | 1.057143 | 0.380952 | 15.209524 | 1.361905 | 0.000000 | 16.571429 | 17.371429 | 0.236776 | 0.423986 | 0.814421 | 0.968847 | 0.518191 | 0.788043 | 0.539120 | 0.546402 | 0.606796 | 0.690141 | 0.558720 | 0.134058 | 0.125392 | 0.478717 | 0.275530 | 0.000000 | 0.435981 | 0.405965 |
| 6 | Gilbert Burns | Orthodox | No | No | Yes | No | Gilbert Burns | 0.041711 | 0.530710 | 0.188464 | 0.000000 | 1.051308 | 8.479374 | 3.693736 | 3.561956 | 12.524293 | 1.535371 | 1.675401 | 15.735065 | 30.133766 | 24.078151 | 39.108938 | 1.951566 | 21.409015 | 4.409702 | 4.315050 | 26.107945 | 1.971811 | 2.054011 | 0.093659 | 0.130176 | 0.009091 | 0.000000 | 0.492265 | 11.871429 | 4.073415 | 1.743392 | 15.548358 | 1.317189 | 0.822689 | 17.688235 | 36.805806 | 22.144003 | 41.636440 | 0.353934 | 29.493736 | 5.339801 | 1.972269 | 34.036440 | 1.608785 | 1.160581 | 0.223759 | 17.622307 | 1.266387 | 0.228877 | 18.488083 | 0.291597 | 0.337892 | 19.117571 | 19.492437 | 0.271941 | 0.396066 | 0.837638 | 0.825473 | 0.479712 | 0.778660 | 0.815673 | 0.522174 | 0.615669 | 0.632204 | 0.597493 | 0.237160 | 0.116048 | 0.543185 | 0.181253 | 0.291140 | 0.519417 | 0.468158 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2245 | Pedro Rizzo | Orthodox | No | No | Yes | No | Pedro Rizzo | 0.103333 | 0.045000 | 0.000000 | 0.000000 | 0.546778 | 3.351667 | 0.490000 | 2.723333 | 4.548333 | 0.935000 | 1.081667 | 6.565000 | 19.076667 | 10.651667 | 23.543333 | 0.163333 | 14.890000 | 0.490000 | 3.696667 | 15.856667 | 1.390000 | 1.830000 | 0.020000 | 0.408333 | 0.040000 | 0.000000 | 0.769444 | 5.956667 | 0.595000 | 2.345000 | 5.720000 | 1.935000 | 1.241667 | 8.896667 | 19.263333 | 16.455000 | 27.180000 | 1.963333 | 15.356667 | 0.823333 | 3.083333 | 14.441667 | 2.881667 | 1.940000 | 1.555000 | 9.400000 | 0.228333 | 0.738333 | 8.721667 | 0.946667 | 0.698333 | 10.366667 | 10.725000 | 0.275510 | 0.225095 | 1.000000 | 0.736700 | 0.286840 | 0.672662 | 0.591075 | 0.344138 | 0.452428 | 0.792020 | 0.612112 | 0.277328 | 0.239459 | 0.603924 | 0.328514 | 0.359966 | 0.538155 | 0.394592 |
| 2251 | Gan McGee | Orthodox | No | No | Yes | No | Gan McGee | 0.000000 | 0.625000 | 0.000000 | 0.000000 | 2.116667 | 5.250000 | 0.500000 | 0.750000 | 3.625000 | 1.000000 | 1.875000 | 6.500000 | 17.375000 | 8.750000 | 19.750000 | 1.750000 | 16.000000 | 0.500000 | 0.875000 | 12.500000 | 1.125000 | 3.750000 | 0.125000 | 0.500000 | 0.625000 | 0.000000 | 0.900000 | 6.750000 | 2.125000 | 2.375000 | 3.000000 | 2.750000 | 5.500000 | 11.250000 | 19.875000 | 22.500000 | 32.375000 | 1.375000 | 15.375000 | 2.125000 | 2.375000 | 9.500000 | 3.250000 | 7.125000 | 0.875000 | 8.625000 | 0.000000 | 0.000000 | 6.500000 | 0.500000 | 1.625000 | 8.625000 | 9.875000 | 0.357143 | 0.328125 | 1.000000 | 0.857143 | 0.290000 | 0.888889 | 0.500000 | 0.374101 | 0.443038 | 0.636364 | 0.560976 | 0.000000 | 0.000000 | 0.684211 | 0.153846 | 0.228070 | 0.433962 | 0.305019 |
| 2252 | Ian Freeman | Orthodox | No | No | No | No | Ian Freeman | 0.000000 | 0.244444 | 0.166667 | 0.066667 | 1.942407 | 9.344444 | 0.644444 | 0.311111 | 1.122222 | 2.844444 | 6.333333 | 10.300000 | 21.322222 | 22.833333 | 34.544444 | 0.644444 | 20.166667 | 0.844444 | 0.311111 | 5.377778 | 4.722222 | 11.222222 | 0.066667 | 0.266667 | 0.200000 | 0.177778 | 1.447778 | 3.822222 | 2.933333 | 2.611111 | 2.511111 | 3.622222 | 3.233333 | 9.366667 | 16.200000 | 17.966667 | 25.333333 | 2.488889 | 8.922222 | 3.966667 | 3.311111 | 5.755556 | 5.522222 | 4.922222 | 2.222222 | 5.100000 | 1.033333 | 0.700000 | 3.244444 | 1.900000 | 1.688889 | 6.833333 | 7.366667 | 0.379310 | 0.463361 | 0.763158 | 1.000000 | 0.208678 | 0.602353 | 0.564356 | 0.483064 | 0.660984 | 0.892857 | 0.571606 | 0.260504 | 0.211409 | 0.563707 | 0.344064 | 0.343115 | 0.421811 | 0.290789 |
| 2264 | Eugene Jackson | Orthodox | No | No | No | No | Eugene Jackson | 0.000000 | 0.250000 | 0.750000 | 0.000000 | 0.112500 | 0.750000 | 0.125000 | 0.000000 | 0.625000 | 0.125000 | 0.125000 | 0.875000 | 3.250000 | 9.875000 | 12.625000 | 0.750000 | 3.000000 | 0.250000 | 0.000000 | 2.875000 | 0.125000 | 0.250000 | 0.125000 | 0.875000 | 0.500000 | 0.000000 | 1.822917 | 1.375000 | 0.625000 | 0.750000 | 1.625000 | 0.000000 | 1.125000 | 2.750000 | 4.000000 | 11.000000 | 12.625000 | 1.000000 | 2.250000 | 0.625000 | 1.125000 | 2.125000 | 0.000000 | 1.875000 | 0.125000 | 0.875000 | 0.000000 | 0.375000 | 0.500000 | 0.000000 | 0.750000 | 1.250000 | 1.625000 | 0.333333 | 0.250000 | 0.500000 | NaN | 0.217391 | 1.000000 | 0.500000 | 0.269231 | 0.782178 | 0.125000 | 0.388889 | 0.000000 | 0.333333 | 0.235294 | NaN | 0.400000 | 0.312500 | 0.128713 |
| 2286 | Fabiano Iha | Orthodox | No | No | No | No | Fabiano Iha | 0.000000 | 0.600000 | 0.400000 | 0.000000 | 1.698889 | 3.200000 | 0.200000 | 1.733333 | 1.333333 | 1.933333 | 1.866667 | 5.133333 | 12.466667 | 13.933333 | 23.266667 | 1.533333 | 9.733333 | 0.933333 | 1.800000 | 5.733333 | 3.600000 | 3.133333 | 0.000000 | 0.200000 | 0.000000 | 0.000000 | 1.661111 | 8.866667 | 0.133333 | 0.133333 | 2.066667 | 6.200000 | 0.866667 | 9.133333 | 15.000000 | 26.400000 | 33.066667 | 0.400000 | 14.666667 | 0.133333 | 0.200000 | 4.800000 | 8.866667 | 1.333333 | 0.200000 | 5.800000 | 0.000000 | 0.066667 | 2.733333 | 2.666667 | 0.466667 | 5.866667 | 6.666667 | 0.391304 | 0.328767 | 0.214286 | 0.962963 | 0.232558 | 0.537037 | 0.595745 | 0.411765 | 0.598854 | 0.500000 | 0.395455 | 0.000000 | 0.333333 | 0.569444 | 0.300752 | 0.350000 | 0.391111 | 0.201613 |
1218 rows × 78 columns
# Calculate the mean for each of the specified columns
means3 = ra_non_champions[metrics1].mean()
# Rank the means from highest to lowest and plot again
sorted_means3 = means3.sort_values(ascending=False)
# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_means3.plot(kind='bar', color='blueviolet')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90) # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()
# Calculate the mean for each of the specified columns
meansC = ra_non_champions[metrics2].mean()
# Rank the means from highest to lowest and plot again
sorted_meansC = meansC.sort_values(ascending=False)
# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_meansC.plot(kind='bar', color='mediumvioletred')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)
plt.show()
ra_non_challengers = ra[ra['EVER_CHALLENGER'] != 'Yes']
ra_non_challengers
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | RA_FIGHTER | RA_KD | RA_TD | RA_SUB.ATT | RA_REV | RA_CTRL | RA_HEAD | RA_BODY | RA_LEG | RA_DISTANCE | RA_CLINCH | RA_GROUND | RA_SIG | RA_TOT.SIG | RA_STR | RA_TOTAL.STR | RA_TOTAL.TD | RA_TOTAL.HEAD | RA_TOTAL.BODY | RA_TOTAL.LEG | RA_TOTAL.DISTANCE | RA_TOTAL.CLINCH | RA_TOTAL.GROUND | RA_OPP_KD | RA_OPP_TD | RA_OPP_SUB.ATT | RA_OPP_REV | RA_OPP_CTRL | RA_OPP_HEAD | RA_OPP_BODY | RA_OPP_LEG | RA_OPP_DISTANCE | RA_OPP_CLINCH | RA_OPP_GROUND | RA_OPP_SIG | RA_OPP_TOT.SIG | RA_OPP_STR | RA_OPP_TOTAL.STR | RA_OPP_TOTAL.TD | RA_OPP_TOTAL.HEAD | RA_OPP_TOTAL.BODY | RA_OPP_TOTAL.LEG | RA_OPP_TOTAL.DISTANCE | RA_OPP_TOTAL.CLINCH | RA_OPP_TOTAL.GROUND | RA_TD_DEF | RA_HEAD_DEF | RA_BODY_DEF | RA_LEG_DEF | RA_DISTANCE_DEF | RA_CLINCH_DEF | RA_GROUND_DEF | RA_SIG_DEF | RA_STR_DEF | RA_TD_% | RA_HEAD_% | RA_BODY_% | RA_LEG_% | RA_DISTANCE_% | RA_CLINCH_% | RA_GROUND_% | RA_SIG_% | RA_STR_% | RA_TD_DEF_% | RA_HEAD_DEF_% | RA_BODY_DEF_% | RA_LEG_DEF_% | RA_DISTANCE_DEF_% | RA_CLINCH_DEF_% | RA_GROUND_DEF_% | RA_SIG_DEF_% | RA_STR_DEF_% | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | CJ Vergara | Orthodox | No | No | No | No | CJ Vergara | 0.000000 | 0.000000 | 0.055556 | 0.055556 | 0.681481 | 12.750000 | 9.861111 | 3.083333 | 18.250000 | 2.500000 | 4.944444 | 25.694444 | 44.611111 | 37.111111 | 57.305556 | 0.000000 | 28.472222 | 12.638889 | 3.500000 | 34.111111 | 3.250000 | 7.250000 | 0.111111 | 1.000000 | 0.305556 | 0.138889 | 1.258796 | 14.000000 | 5.777778 | 2.916667 | 19.722222 | 1.527778 | 1.444444 | 22.694444 | 48.111111 | 27.361111 | 54.027778 | 2.555556 | 37.333333 | 7.638889 | 3.138889 | 44.250000 | 2.027778 | 1.833333 | 1.555556 | 23.333333 | 1.861111 | 0.222222 | 24.527778 | 0.500000 | 0.388889 | 25.416667 | 26.666667 | NaN | 0.447805 | 0.780220 | 0.880952 | 0.535016 | 0.769231 | 0.681992 | 0.575965 | 0.647601 | 0.608696 | 0.625000 | 0.243636 | 0.070796 | 0.554300 | 0.246575 | 0.212121 | 0.528291 | 0.493573 |
| 2 | Curtis Blaydes | Orthodox | No | No | No | No | Curtis Blaydes | 0.024444 | 1.886667 | 0.000000 | 0.000000 | 2.670889 | 8.462222 | 1.548889 | 1.500000 | 5.433333 | 1.900000 | 4.177778 | 11.511111 | 21.655556 | 22.500000 | 35.106667 | 3.837778 | 18.253333 | 1.726667 | 1.675556 | 14.168889 | 2.197778 | 5.288889 | 0.046667 | 0.333333 | 0.026667 | 0.000000 | 0.216815 | 5.788889 | 1.411111 | 0.400000 | 6.882222 | 0.542222 | 0.175556 | 7.600000 | 19.677778 | 26.988889 | 40.008889 | 0.613333 | 17.313333 | 1.906667 | 0.457778 | 18.555556 | 0.902222 | 0.220000 | 0.280000 | 11.524444 | 0.495556 | 0.057778 | 11.673333 | 0.360000 | 0.044444 | 12.077778 | 13.020000 | 0.491604 | 0.463599 | 0.897040 | 0.895225 | 0.383469 | 0.864510 | 0.789916 | 0.531555 | 0.640904 | 0.456522 | 0.665640 | 0.259907 | 0.126214 | 0.629102 | 0.399015 | 0.202020 | 0.613778 | 0.325428 |
| 3 | Jailton Almeida | Orthodox | No | No | No | No | Jailton Almeida | 0.000000 | 1.476190 | 0.304762 | 0.200000 | 3.737937 | 8.580952 | 0.571429 | 0.266667 | 0.647619 | 0.600000 | 8.171429 | 9.419048 | 14.228571 | 27.542857 | 35.980952 | 3.409524 | 12.971429 | 0.628571 | 0.628571 | 1.409524 | 1.600000 | 11.219048 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.198730 | 3.676190 | 0.257143 | 0.028571 | 0.323810 | 0.200000 | 3.438095 | 3.961905 | 7.352381 | 5.923810 | 10.219048 | 0.400000 | 6.809524 | 0.514286 | 0.028571 | 1.933333 | 0.200000 | 5.219048 | 0.400000 | 3.133333 | 0.257143 | 0.000000 | 1.609524 | 0.000000 | 1.780952 | 3.390476 | 4.295238 | 0.432961 | 0.661527 | 0.909091 | 0.424242 | 0.459459 | 0.375000 | 0.728353 | 0.661981 | 0.765484 | 1.000000 | 0.460140 | 0.500000 | 0.000000 | 0.832512 | 0.000000 | 0.341241 | 0.461140 | 0.420317 |
| 4 | Benoit Saint Denis | Southpaw | No | No | No | No | Benoit Saint Denis | 0.209524 | 0.895238 | 0.257143 | 0.047619 | 1.433492 | 11.952381 | 6.561905 | 2.961905 | 14.514286 | 2.761905 | 4.200000 | 21.476190 | 39.304762 | 29.761905 | 49.047619 | 3.780952 | 28.190476 | 8.057143 | 3.057143 | 28.009524 | 3.504762 | 7.790476 | 0.066667 | 0.209524 | 0.323810 | 0.000000 | 0.547302 | 11.952381 | 6.828571 | 2.657143 | 16.561905 | 3.580952 | 1.295238 | 21.438095 | 38.009524 | 25.419048 | 42.790476 | 0.676190 | 27.085714 | 7.885714 | 3.038095 | 31.771429 | 4.942857 | 1.295238 | 0.466667 | 15.133333 | 1.057143 | 0.380952 | 15.209524 | 1.361905 | 0.000000 | 16.571429 | 17.371429 | 0.236776 | 0.423986 | 0.814421 | 0.968847 | 0.518191 | 0.788043 | 0.539120 | 0.546402 | 0.606796 | 0.690141 | 0.558720 | 0.134058 | 0.125392 | 0.478717 | 0.275530 | 0.000000 | 0.435981 | 0.405965 |
| 7 | Jack Della Maddalena | Switch | No | No | No | No | Jack Della Maddalena | 0.349206 | 0.111111 | 0.047619 | 0.000000 | 0.215873 | 18.476190 | 7.714286 | 2.714286 | 23.301587 | 2.857143 | 2.746032 | 28.904762 | 51.460317 | 36.714286 | 60.507937 | 0.206349 | 37.650794 | 10.634921 | 3.174603 | 44.555556 | 3.666667 | 3.238095 | 0.000000 | 0.777778 | 0.047619 | 0.111111 | 1.065873 | 10.761905 | 4.111111 | 3.825397 | 17.444444 | 1.206349 | 0.047619 | 18.698413 | 56.587302 | 20.603175 | 58.714286 | 3.301587 | 44.746032 | 7.222222 | 4.619048 | 54.984127 | 1.555556 | 0.047619 | 2.523810 | 33.984127 | 3.111111 | 0.793651 | 37.539683 | 0.349206 | 0.000000 | 37.888889 | 38.111111 | 0.538462 | 0.490725 | 0.725373 | 0.855000 | 0.522978 | 0.779221 | 0.848039 | 0.561690 | 0.606768 | 0.764423 | 0.759489 | 0.430769 | 0.171821 | 0.682737 | 0.224490 | 0.000000 | 0.669565 | 0.649094 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2198 | Trevor Prangley | Orthodox | No | No | No | No | Trevor Prangley | 0.000000 | 1.277778 | 0.388889 | 0.000000 | 2.418056 | 4.083333 | 2.555556 | 3.083333 | 2.000000 | 4.888889 | 2.833333 | 9.722222 | 16.583333 | 26.611111 | 34.944444 | 1.972222 | 10.777778 | 2.722222 | 3.083333 | 6.444444 | 5.611111 | 4.527778 | 0.000000 | 0.638889 | 0.083333 | 0.000000 | 1.337037 | 3.500000 | 2.222222 | 1.000000 | 2.611111 | 2.444444 | 1.666667 | 6.722222 | 13.722222 | 19.333333 | 29.444444 | 2.555556 | 9.777778 | 2.944444 | 1.000000 | 5.611111 | 4.361111 | 3.750000 | 1.916667 | 6.277778 | 0.722222 | 0.000000 | 3.000000 | 1.916667 | 2.083333 | 7.000000 | 10.111111 | 0.647887 | 0.378866 | 0.938776 | 1.000000 | 0.310345 | 0.871287 | 0.625767 | 0.586265 | 0.761526 | 0.750000 | 0.642045 | 0.245283 | 0.000000 | 0.534653 | 0.439490 | 0.555556 | 0.510121 | 0.343396 |
| 2234 | Wesley Correira | Orthodox | No | No | No | No | Wesley Correira | 0.125000 | 0.000000 | 0.000000 | 0.000000 | 0.045139 | 7.833333 | 0.666667 | 0.416667 | 4.291667 | 4.500000 | 0.125000 | 8.916667 | 21.791667 | 17.166667 | 30.041667 | 0.166667 | 20.333333 | 0.666667 | 0.791667 | 13.416667 | 8.250000 | 0.125000 | 0.166667 | 0.000000 | 0.000000 | 0.000000 | 0.643056 | 23.166667 | 1.958333 | 3.125000 | 17.833333 | 10.416667 | 0.000000 | 28.250000 | 46.291667 | 32.541667 | 50.833333 | 1.208333 | 39.416667 | 2.666667 | 4.208333 | 30.583333 | 15.708333 | 0.000000 | 1.208333 | 16.250000 | 0.708333 | 1.083333 | 12.750000 | 5.291667 | 0.000000 | 18.041667 | 18.291667 | 0.000000 | 0.385246 | 1.000000 | 0.526316 | 0.319876 | 0.545455 | 1.000000 | 0.409178 | 0.571429 | 1.000000 | 0.412262 | 0.265625 | 0.257426 | 0.416894 | 0.336870 | NaN | 0.389739 | 0.359836 |
| 2252 | Ian Freeman | Orthodox | No | No | No | No | Ian Freeman | 0.000000 | 0.244444 | 0.166667 | 0.066667 | 1.942407 | 9.344444 | 0.644444 | 0.311111 | 1.122222 | 2.844444 | 6.333333 | 10.300000 | 21.322222 | 22.833333 | 34.544444 | 0.644444 | 20.166667 | 0.844444 | 0.311111 | 5.377778 | 4.722222 | 11.222222 | 0.066667 | 0.266667 | 0.200000 | 0.177778 | 1.447778 | 3.822222 | 2.933333 | 2.611111 | 2.511111 | 3.622222 | 3.233333 | 9.366667 | 16.200000 | 17.966667 | 25.333333 | 2.488889 | 8.922222 | 3.966667 | 3.311111 | 5.755556 | 5.522222 | 4.922222 | 2.222222 | 5.100000 | 1.033333 | 0.700000 | 3.244444 | 1.900000 | 1.688889 | 6.833333 | 7.366667 | 0.379310 | 0.463361 | 0.763158 | 1.000000 | 0.208678 | 0.602353 | 0.564356 | 0.483064 | 0.660984 | 0.892857 | 0.571606 | 0.260504 | 0.211409 | 0.563707 | 0.344064 | 0.343115 | 0.421811 | 0.290789 |
| 2264 | Eugene Jackson | Orthodox | No | No | No | No | Eugene Jackson | 0.000000 | 0.250000 | 0.750000 | 0.000000 | 0.112500 | 0.750000 | 0.125000 | 0.000000 | 0.625000 | 0.125000 | 0.125000 | 0.875000 | 3.250000 | 9.875000 | 12.625000 | 0.750000 | 3.000000 | 0.250000 | 0.000000 | 2.875000 | 0.125000 | 0.250000 | 0.125000 | 0.875000 | 0.500000 | 0.000000 | 1.822917 | 1.375000 | 0.625000 | 0.750000 | 1.625000 | 0.000000 | 1.125000 | 2.750000 | 4.000000 | 11.000000 | 12.625000 | 1.000000 | 2.250000 | 0.625000 | 1.125000 | 2.125000 | 0.000000 | 1.875000 | 0.125000 | 0.875000 | 0.000000 | 0.375000 | 0.500000 | 0.000000 | 0.750000 | 1.250000 | 1.625000 | 0.333333 | 0.250000 | 0.500000 | NaN | 0.217391 | 1.000000 | 0.500000 | 0.269231 | 0.782178 | 0.125000 | 0.388889 | 0.000000 | 0.333333 | 0.235294 | NaN | 0.400000 | 0.312500 | 0.128713 |
| 2286 | Fabiano Iha | Orthodox | No | No | No | No | Fabiano Iha | 0.000000 | 0.600000 | 0.400000 | 0.000000 | 1.698889 | 3.200000 | 0.200000 | 1.733333 | 1.333333 | 1.933333 | 1.866667 | 5.133333 | 12.466667 | 13.933333 | 23.266667 | 1.533333 | 9.733333 | 0.933333 | 1.800000 | 5.733333 | 3.600000 | 3.133333 | 0.000000 | 0.200000 | 0.000000 | 0.000000 | 1.661111 | 8.866667 | 0.133333 | 0.133333 | 2.066667 | 6.200000 | 0.866667 | 9.133333 | 15.000000 | 26.400000 | 33.066667 | 0.400000 | 14.666667 | 0.133333 | 0.200000 | 4.800000 | 8.866667 | 1.333333 | 0.200000 | 5.800000 | 0.000000 | 0.066667 | 2.733333 | 2.666667 | 0.466667 | 5.866667 | 6.666667 | 0.391304 | 0.328767 | 0.214286 | 0.962963 | 0.232558 | 0.537037 | 0.595745 | 0.411765 | 0.598854 | 0.500000 | 0.395455 | 0.000000 | 0.333333 | 0.569444 | 0.300752 | 0.350000 | 0.391111 | 0.201613 |
1108 rows × 78 columns
# Calculate the mean for each of the specified columns
means4 = ra_non_challengers[metrics1].mean()
# Rank the means from highest to lowest and plot again
sorted_means4 = means4.sort_values(ascending=False)
# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_means4.plot(kind='bar', color='darkorange')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90) # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()
# Calculate the mean for each of the specified columns
meansD = ra_non_challengers[metrics2].mean()
# Rank the means from highest to lowest and plot again
sorted_meansD = meansD.sort_values(ascending=False)
# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_meansD.plot(kind='bar', color='lightcoral')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)
plt.show()
# # Display max rows
# pd.set_option('display.max_rows', None)
# breakdown(records)
# # Set the display options to show 20 rows at the top and bottom
# pd.set_option('display.max_rows', 20)
# Filter the dataframe using the correct column name "FIGHTER"
r = records[records['FIGHTER'].isin(fighters_list)]
r.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 1321 entries, 1 to 2286 Data columns (total 46 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 FIGHTER 1321 non-null object 1 STANCE 1302 non-null object 2 EVER_CHAMPION 1321 non-null object 3 EVER_INTERIM 1321 non-null object 4 EVER_CHALLENGER 1321 non-null object 5 EITHER_CHAMP 1321 non-null object 6 Total_Rounds 1321 non-null int64 7 Total_Fights 1321 non-null int64 8 Wins 1321 non-null int64 9 Losses 1321 non-null int64 10 Draws 1321 non-null int64 11 No_Contests 1321 non-null int64 12 Win_by_KO_TKO 1321 non-null int64 13 Win_by_Submission 1321 non-null int64 14 Win_by_Decision 1321 non-null int64 15 Loss_by_KO_TKO 1321 non-null int64 16 Loss_by_Submission 1321 non-null int64 17 Loss_by_Decision 1321 non-null int64 18 Total_Fights_with_Title 1321 non-null float64 19 Wins_with_Title 1321 non-null float64 20 Losses_with_Title 1321 non-null float64 21 Draws_with_Title 1321 non-null float64 22 No_Contests_with_Title 1321 non-null float64 23 Win_by_KO_TKO_with_Title 1321 non-null float64 24 Win_by_Submission_with_Title 1321 non-null float64 25 Win_by_Decision_with_Title 1321 non-null float64 26 Loss_by_KO_TKO_with_Title 1321 non-null float64 27 Loss_by_Submission_with_Title 1321 non-null float64 28 Loss_by_Decision_with_Title 1321 non-null float64 29 Win_% 1321 non-null float64 30 Loss_% 1321 non-null float64 31 KO_Win_% 1321 non-null float64 32 Sub_Win_% 1321 non-null float64 33 Dec_Win_% 1321 non-null float64 34 KO_Loss_% 1321 non-null float64 35 Sub_Loss_% 1321 non-null float64 36 Dec_Loss_% 1321 non-null float64 37 Title_Win_% 213 non-null float64 38 Title_Loss_% 213 non-null float64 39 Title_KO_Win_% 213 non-null float64 40 Title_Sub_Win_% 213 non-null float64 41 Title_Dec_Win_% 213 non-null float64 42 Title_KO_Loss_% 213 non-null float64 43 Title_Sub_Loss_% 213 non-null float64 44 Title_Dec_Loss_% 213 non-null float64 45 Rounds_per_Fight 1321 non-null float64 dtypes: float64(28), int64(12), object(6) memory usage: 485.1+ KB
r.describe()
| Total_Rounds | Total_Fights | Wins | Losses | Draws | No_Contests | Win_by_KO_TKO | Win_by_Submission | Win_by_Decision | Loss_by_KO_TKO | Loss_by_Submission | Loss_by_Decision | Total_Fights_with_Title | Wins_with_Title | Losses_with_Title | Draws_with_Title | No_Contests_with_Title | Win_by_KO_TKO_with_Title | Win_by_Submission_with_Title | Win_by_Decision_with_Title | Loss_by_KO_TKO_with_Title | Loss_by_Submission_with_Title | Loss_by_Decision_with_Title | Win_% | Loss_% | KO_Win_% | Sub_Win_% | Dec_Win_% | KO_Loss_% | Sub_Loss_% | Dec_Loss_% | Title_Win_% | Title_Loss_% | Title_KO_Win_% | Title_Sub_Win_% | Title_Dec_Win_% | Title_KO_Loss_% | Title_Sub_Loss_% | Title_Dec_Loss_% | Rounds_per_Fight | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 1321.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 1321.000000 |
| mean | 22.987131 | 9.562453 | 5.096139 | 4.295231 | 0.071915 | 0.099167 | 1.693414 | 0.996215 | 2.393641 | 1.385314 | 0.794095 | 2.104466 | 0.498107 | 0.245269 | 0.242241 | 0.009084 | 0.001514 | 0.105980 | 0.043149 | 0.095382 | 0.103709 | 0.042392 | 0.095382 | 49.161012 | 49.017425 | 15.604412 | 9.774328 | 23.633311 | 15.720207 | 9.388340 | 23.801335 | 28.978448 | 69.047172 | 12.364603 | 5.305903 | 11.214045 | 28.471005 | 12.566713 | 27.892083 | 2.365330 |
| std | 15.705837 | 5.941195 | 4.092750 | 2.499524 | 0.272708 | 0.313834 | 2.073431 | 1.530741 | 2.349614 | 1.429896 | 1.086415 | 1.813582 | 1.675638 | 1.165974 | 0.677832 | 0.094912 | 0.038895 | 0.544367 | 0.312056 | 0.562719 | 0.399613 | 0.229667 | 0.387281 | 19.507356 | 19.492936 | 15.628908 | 12.857489 | 17.870368 | 15.927979 | 13.340309 | 18.691333 | 33.492435 | 34.532522 | 21.022367 | 13.716553 | 21.156069 | 37.177658 | 28.624965 | 38.650714 | 0.448269 |
| min | 4.000000 | 4.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 |
| 25% | 12.000000 | 5.000000 | 2.000000 | 3.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 35.714286 | 36.363636 | 0.000000 | 0.000000 | 10.526316 | 0.000000 | 0.000000 | 10.000000 | 0.000000 | 40.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 2.055556 |
| 50% | 18.000000 | 8.000000 | 4.000000 | 4.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 2.000000 | 1.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 50.000000 | 50.000000 | 14.285714 | 0.000000 | 23.529412 | 13.636364 | 0.000000 | 22.222222 | 0.000000 | 80.000000 | 0.000000 | 0.000000 | 0.000000 | 8.333333 | 0.000000 | 0.000000 | 2.400000 |
| 75% | 30.000000 | 12.000000 | 7.000000 | 5.000000 | 0.000000 | 0.000000 | 2.000000 | 1.000000 | 3.000000 | 2.000000 | 1.000000 | 3.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 61.538462 | 61.538462 | 25.000000 | 17.647059 | 33.333333 | 25.000000 | 15.384615 | 33.333333 | 55.555556 | 100.000000 | 20.000000 | 0.000000 | 16.666667 | 50.000000 | 0.000000 | 50.000000 | 2.666667 |
| max | 111.000000 | 43.000000 | 26.000000 | 18.000000 | 2.000000 | 2.000000 | 14.000000 | 16.000000 | 14.000000 | 8.000000 | 7.000000 | 12.000000 | 16.000000 | 15.000000 | 6.000000 | 1.000000 | 1.000000 | 7.000000 | 5.000000 | 8.000000 | 6.000000 | 3.000000 | 5.000000 | 100.000000 | 100.000000 | 75.000000 | 83.333333 | 100.000000 | 83.333333 | 80.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 75.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 3.833333 |
metrics1 = ['Total_Rounds', 'Total_Fights', 'Wins', 'Losses', 'Draws', 'No_Contests', 'Win_by_KO_TKO',
'Win_by_Submission', 'Win_by_Decision', 'Loss_by_KO_TKO', 'Loss_by_Submission', 'Loss_by_Decision',
'Total_Fights_with_Title', 'Wins_with_Title', 'Losses_with_Title', 'Draws_with_Title', 'No_Contests_with_Title',
'Win_by_KO_TKO_with_Title', 'Win_by_Submission_with_Title',
'Win_by_Decision_with_Title', 'Loss_by_KO_TKO_with_Title', 'Loss_by_Submission_with_Title',
'Loss_by_Decision_with_Title', 'Rounds_per_Fight']
metrics2 = ['Win_%', 'Loss_%', 'KO_Win_%', 'Sub_Win_%', 'Dec_Win_%',
'KO_Loss_%', 'Sub_Loss_%', 'Dec_Loss_%', 'Title_Win_%', 'Title_Loss_%',
'Title_KO_Win_%', 'Title_Sub_Win_%', 'Title_Dec_Win_%', 'Title_KO_Loss_%','Title_Sub_Loss_%',
'Title_Dec_Loss_%']
# Original metrics list
metrics = ['Total_Rounds', 'Total_Fights', 'Wins', 'Losses', 'Draws', 'No_Contests', 'Win_by_KO_TKO',
'Win_by_Submission', 'Win_by_Decision', 'Loss_by_KO_TKO', 'Loss_by_Submission', 'Loss_by_Decision',
'Total_Fights_with_Title', 'Wins_with_Title', 'Losses_with_Title', 'Draws_with_Title', 'No_Contests_with_Title',
'Win_by_KO_TKO_with_Title', 'Win_by_Submission_with_Title',
'Win_by_Decision_with_Title', 'Loss_by_KO_TKO_with_Title', 'Loss_by_Submission_with_Title',
'Loss_by_Decision_with_Title', 'Rounds_per_Fight', 'Win_%', 'Loss_%', 'KO_Win_%', 'Sub_Win_%', 'Dec_Win_%',
'KO_Loss_%', 'Sub_Loss_%', 'Dec_Loss_%', 'Title_Win_%', 'Title_Loss_%',
'Title_KO_Win_%', 'Title_Sub_Win_%', 'Title_Dec_Win_%', 'Title_KO_Loss_%','Title_Sub_Loss_%',
'Title_Dec_Loss_%']
# Reinitializing the dictionary to hold the top ten fighters for the updated list of metrics
top_fighters_per_metric = {}
# Creating a dictionary to hold the top ten fighters for each metric
top_fighters_per_metric = {}
# Finding the top ten fighters for each metric
for metric in metrics:
# Some metrics may have missing values, so we sort only non-NA values
top_fighters_per_metric[metric] = r.nlargest(10, metric)[['FIGHTER', metric]]
# Printing the top ten fighters for each metric with a blank line in between
for metric, data in top_fighters_per_metric.items():
print(f"Top ten fighters for {metric}:")
if isinstance(data, pd.DataFrame):
print(data.to_string(index=False))
else:
print(data)
print("\n") # Blank line for separation
Top ten fighters for Total_Rounds:
FIGHTER Total_Rounds
Rafael Dos Anjos 111
Frankie Edgar 99
Jim Miller 96
Andrei Arlovski 92
Clay Guida 92
Max Holloway 90
Demian Maia 89
Neil Magny 87
Jeremy Stephens 86
Diego Sanchez 84
Top ten fighters for Total_Fights:
FIGHTER Total_Fights
Jim Miller 43
Andrei Arlovski 41
Donald Cerrone 38
Clay Guida 36
Rafael Dos Anjos 35
Jeremy Stephens 34
Demian Maia 33
Neil Magny 32
Charles Oliveira 32
Diego Sanchez 32
Top ten fighters for Wins:
FIGHTER Wins
Jim Miller 26
Andrei Arlovski 23
Donald Cerrone 23
Dustin Poirier 22
Neil Magny 22
Charles Oliveira 22
Demian Maia 22
Rafael Dos Anjos 21
Jon Jones 21
Max Holloway 20
Top ten fighters for Losses:
FIGHTER Losses
Clay Guida 18
Jeremy Stephens 18
Andrei Arlovski 17
Jim Miller 16
Michael Johnson 15
Rafael Dos Anjos 14
Donald Cerrone 14
Angela Hill 13
Matt Brown 13
Sam Alvey 13
Top ten fighters for Draws:
FIGHTER Draws
Brandon Moreno 2
Marina Rodriguez 2
Lando Vannata 2
BJ Penn 2
Caol Uno 2
Ion Cutelaba 1
Song Yadong 1
Ludovit Klein 1
Charles Jourdain 1
Sean Woodson 1
Top ten fighters for No_Contests:
FIGHTER No_Contests
Pedro Munhoz 2
Niko Price 2
Darren Stewart 2
Kevin Casey 2
Thiago Silva 2
Matthew Riddle 2
Curtis Blaydes 1
Dustin Poirier 1
Kevin Holland 1
Michal Oleksiejczuk 1
Top ten fighters for Win_by_KO_TKO:
FIGHTER Win_by_KO_TKO
Derrick Lewis 14
Matt Brown 13
Dustin Poirier 11
Thiago Santos 11
Anderson Silva 11
Anthony Johnson 11
Donald Cerrone 10
Francis Ngannou 10
Junior Dos Santos 10
Cain Velasquez 10
Top ten fighters for Win_by_Submission:
FIGHTER Win_by_Submission
Charles Oliveira 16
Jim Miller 12
Demian Maia 11
Nate Diaz 10
Gerald Meerschaert 9
Gunnar Nelson 8
Frank Mir 8
Gillian Robertson 7
Brendan Allen 7
Rani Yahya 7
Top ten fighters for Win_by_Decision:
FIGHTER Win_by_Decision
Neil Magny 14
Brad Tavares 13
Rafael Dos Anjos 12
Andrei Arlovski 12
Diego Sanchez 12
Georges St-Pierre 12
Robert Whittaker 11
Clay Guida 11
Darren Elkins 11
Belal Muhammad 11
Top ten fighters for Loss_by_KO_TKO:
FIGHTER Loss_by_KO_TKO
Andrei Arlovski 8
Donald Cerrone 8
Alistair Overeem 8
Stefan Struve 8
Gabriel Gonzaga 8
Frank Mir 8
Derrick Lewis 7
Junior Dos Santos 7
Ben Saunders 7
Jake Ellenberger 7
Top ten fighters for Loss_by_Submission:
FIGHTER Loss_by_Submission
Clay Guida 7
Melvin Guillard 6
Joanne Wood 5
Marcos Rogerio de Lima 5
Michael Johnson 5
Neil Magny 5
Tim Means 5
Cub Swanson 5
Michael Chiesa 5
Alex Caceres 5
Top ten fighters for Loss_by_Decision:
FIGHTER Loss_by_Decision
Jeremy Stephens 12
Jim Miller 11
Angela Hill 11
Rafael Dos Anjos 10
Pedro Munhoz 9
Clay Guida 9
Jorge Masvidal 9
Nate Diaz 9
Jessica Eye 9
Demian Maia 9
Top ten fighters for Total_Fights_with_Title:
FIGHTER Total_Fights_with_Title
Jon Jones 16.0
Georges St-Pierre 15.0
Demetrious Johnson 14.0
Randy Couture 14.0
Anderson Silva 13.0
Amanda Nunes 12.0
Jose Aldo 12.0
Matt Hughes 12.0
Valentina Shevchenko 11.0
Israel Adesanya 11.0
Top ten fighters for Wins_with_Title:
FIGHTER Wins_with_Title
Jon Jones 15.0
Georges St-Pierre 13.0
Demetrious Johnson 12.0
Amanda Nunes 11.0
Anderson Silva 11.0
Matt Hughes 9.0
Valentina Shevchenko 8.0
Israel Adesanya 8.0
Jose Aldo 8.0
Randy Couture 8.0
Top ten fighters for Losses_with_Title:
FIGHTER Losses_with_Title
Randy Couture 6.0
Frankie Edgar 5.0
BJ Penn 5.0
Holly Holm 4.0
Max Holloway 4.0
Jose Aldo 4.0
Joanna Jedrzejczyk 4.0
Urijah Faber 4.0
Vitor Belfort 4.0
Tim Sylvia 4.0
Top ten fighters for Draws_with_Title:
FIGHTER Draws_with_Title
Brandon Moreno 1.0
Magomed Ankalaev 1.0
Stephen Thompson 1.0
Deiveson Figueiredo 1.0
Alexa Grasso 1.0
Valentina Shevchenko 1.0
Jan Blachowicz 1.0
Frankie Edgar 1.0
Tyron Woodley 1.0
BJ Penn 1.0
Top ten fighters for No_Contests_with_Title:
FIGHTER No_Contests_with_Title
Jon Jones 1.0
Daniel Cormier 1.0
CJ Vergara 0.0
Curtis Blaydes 0.0
Jailton Almeida 0.0
Benoit Saint Denis 0.0
Dustin Poirier 0.0
Gilbert Burns 0.0
Jack Della Maddalena 0.0
Ion Cutelaba 0.0
Top ten fighters for Win_by_KO_TKO_with_Title:
FIGHTER Win_by_KO_TKO_with_Title
Anderson Silva 7.0
TJ Dillashaw 5.0
Matt Hughes 5.0
Randy Couture 5.0
Chuck Liddell 5.0
Valentina Shevchenko 4.0
Amanda Nunes 4.0
Max Holloway 4.0
Stipe Miocic 4.0
Henry Cejudo 3.0
Top ten fighters for Win_by_Submission_with_Title:
FIGHTER Win_by_Submission_with_Title
Demetrious Johnson 5.0
Jon Jones 4.0
Khabib Nurmagomedov 3.0
Daniel Cormier 3.0
BJ Penn 3.0
Ronda Rousey 3.0
Matt Hughes 3.0
Deiveson Figueiredo 2.0
Amanda Nunes 2.0
Anthony Pettis 2.0
Top ten fighters for Win_by_Decision_with_Title:
FIGHTER Win_by_Decision_with_Title
Jon Jones 8.0
Georges St-Pierre 8.0
Jose Aldo 6.0
Israel Adesanya 5.0
Amanda Nunes 5.0
Demetrious Johnson 5.0
Alexander Volkanovski 4.0
Valentina Shevchenko 4.0
Dominick Cruz 4.0
Joanna Jedrzejczyk 4.0
Top ten fighters for Loss_by_KO_TKO_with_Title:
FIGHTER Loss_by_KO_TKO_with_Title
Randy Couture 6.0
Jose Aldo 4.0
Vitor Belfort 3.0
Frank Mir 3.0
Cody Garbrandt 2.0
Jessica Andrade 2.0
TJ Dillashaw 2.0
Luke Rockhold 2.0
Chan Sung Jung 2.0
Stipe Miocic 2.0
Top ten fighters for Loss_by_Submission_with_Title:
FIGHTER Loss_by_Submission_with_Title
Tim Sylvia 3.0
Dustin Poirier 2.0
Miesha Tate 2.0
Anthony Johnson 2.0
Matt Hughes 2.0
Frank Trigg 2.0
Alex Perez 1.0
Josh Emmett 1.0
Deiveson Figueiredo 1.0
Derrick Lewis 1.0
Top ten fighters for Loss_by_Decision_with_Title:
FIGHTER Loss_by_Decision_with_Title
Frankie Edgar 5.0
Max Holloway 4.0
Joanna Jedrzejczyk 3.0
Urijah Faber 3.0
BJ Penn 3.0
Brandon Moreno 2.0
Colby Covington 2.0
Israel Adesanya 2.0
Holly Holm 2.0
Glover Teixeira 2.0
Top ten fighters for Win_%:
FIGHTER Win_%
Jack Della Maddalena 100.0
Muhammad Mokaev 100.0
Umar Nurmagomedov 100.0
Vitor Petrino 100.0
Ilia Topuria 100.0
Natalia Silva 100.0
Movsar Evloev 100.0
Dricus Du Plessis 100.0
Shavkat Rakhmonov 100.0
Paddy Pimblett 100.0
Top ten fighters for Loss_%:
FIGHTER Loss_%
Zarah Fairn 100.0
JP Buys 100.0
Istela Nunes 100.0
Aaron Phillips 100.0
Alen Amedovski 100.0
Alan Baudot 100.0
Sean Soriano 100.0
Jerome Rivera 100.0
Martin Day 100.0
Vinicius Moreira 100.0
Top ten fighters for KO_Win_%:
FIGHTER KO_Win_%
Sergei Pavlovich 75.000000
Danaa Batgerel 75.000000
Gerald Harris 75.000000
Francis Ngannou 71.428571
Ricco Rodriguez 71.428571
Steve Garcia 66.666667
Cain Velasquez 66.666667
Shane Carwin 66.666667
Gregory Rodrigues 62.500000
Tom Aspinall 62.500000
Top ten fighters for Sub_Win_%:
FIGHTER Sub_Win_%
Shavkat Rakhmonov 83.333333
Rodolfo Vieira 71.428571
Muhammad Mokaev 60.000000
Erin Blanchfield 60.000000
Manny Bermudez 60.000000
Paul Sass 60.000000
Tatiana Suarez 57.142857
Brendan Allen 53.846154
Gunnar Nelson 53.333333
Anthony Hernandez 50.000000
Top ten fighters for Dec_Win_%:
FIGHTER Dec_Win_%
Movsar Evloev 100.0
Tracy Cortez 100.0
Caio Borralho 80.0
Javid Basharat 75.0
Merab Dvalishvili 75.0
Waldo Cortes-Acosta 75.0
Norma Dumont 75.0
Arjan Bhullar 75.0
Jordan Johnson 75.0
Jeremy Kennedy 75.0
Top ten fighters for KO_Loss_%:
FIGHTER KO_Loss_%
Ike Villanueva 83.333333
Marvin Eastman 80.000000
Ken Shamrock 80.000000
Claudio Ribeiro 75.000000
JP Buys 75.000000
Victoria Leonardo 75.000000
Alan Baudot 75.000000
Vinicius Moreira 75.000000
Charles Byrd 75.000000
Chris de la Rocha 75.000000
Top ten fighters for Sub_Loss_%:
FIGHTER Sub_Loss_%
John Albert 80.000000
Tom Niinimaki 75.000000
Mike Guymon 75.000000
Emily Whitmire 66.666667
Brandon Thatch 66.666667
Ryan Jensen 62.500000
Sean Soriano 60.000000
Alex Chambers 60.000000
Shannon Gugerty 60.000000
Pete Spratt 57.142857
Top ten fighters for Dec_Loss_%:
FIGHTER Dec_Loss_%
Jodie Esquibel 100.000000
Terrion Ware 100.000000
Michihiro Omigawa 85.714286
Steve Cantwell 83.333333
Jordan Griffin 80.000000
John Alessio 80.000000
Aleksa Camur 75.000000
Maria Oliveira 75.000000
Aaron Phillips 75.000000
Kay Hansen 75.000000
Top ten fighters for Title_Win_%:
FIGHTER Title_Win_%
Sean O'Malley 100.0
Ilia Topuria 100.0
Dricus Du Plessis 100.0
Alexandre Pantoja 100.0
Leon Edwards 100.0
Tom Aspinall 100.0
Islam Makhachev 100.0
Jamahal Hill 100.0
Khabib Nurmagomedov 100.0
Josh Barnett 100.0
Top ten fighters for Title_Loss_%:
FIGHTER Title_Loss_%
Gilbert Burns 100.0
Marlon Vera 100.0
Alex Perez 100.0
Brandon Royval 100.0
Brian Ortega 100.0
Amanda Lemos 100.0
Paulo Costa 100.0
Mayra Bueno Silva 100.0
Irene Aldana 100.0
Josh Emmett 100.0
Top ten fighters for Title_KO_Win_%:
FIGHTER Title_KO_Win_%
Ilia Topuria 100.000000
Tom Aspinall 100.000000
Josh Barnett 100.000000
Conor McGregor 75.000000
Chuck Liddell 71.428571
Alex Pereira 66.666667
TJ Dillashaw 62.500000
Chris Weidman 60.000000
Anderson Silva 53.846154
Sean O'Malley 50.000000
Top ten fighters for Title_Sub_Win_%:
FIGHTER Title_Sub_Win_%
Khabib Nurmagomedov 75.000000
Pat Miletich 66.666667
Yair Rodriguez 50.000000
Tony Ferguson 50.000000
Jiri Prochazka 50.000000
Alexa Grasso 50.000000
Islam Makhachev 50.000000
Julianna Pena 50.000000
Anthony Pettis 50.000000
Antonio Rodrigo Nogueira 50.000000
Top ten fighters for Title_Dec_Win_%:
FIGHTER Title_Dec_Win_%
Dricus Du Plessis 100.000000
Alexandre Pantoja 100.000000
Jamahal Hill 100.000000
Jens Pulver 100.000000
Benson Henderson 80.000000
Leon Edwards 66.666667
Dominick Cruz 66.666667
Georges St-Pierre 53.333333
Sean O'Malley 50.000000
Alexander Volkanovski 50.000000
Top ten fighters for Title_KO_Loss_%:
FIGHTER Title_KO_Loss_%
Gilbert Burns 100.0
Paulo Costa 100.0
Sergei Pavlovich 100.0
Volkan Oezdemir 100.0
Kai Kara-France 100.0
Jessica Penne 100.0
Lauren Murphy 100.0
Michael Chandler 100.0
Katlyn Chookagian 100.0
Jessica Eye 100.0
Top ten fighters for Title_Sub_Loss_%:
FIGHTER Title_Sub_Loss_%
Alex Perez 100.0
Josh Emmett 100.0
Kevin Lee 100.0
Darren Till 100.0
Megan Anderson 100.0
Ray Borg 100.0
Wilson Reis 100.0
Cat Zingano 100.0
John Moraga 100.0
Anthony Johnson 100.0
Top ten fighters for Title_Dec_Loss_%:
FIGHTER Title_Dec_Loss_%
Marlon Vera 100.0
Brandon Royval 100.0
Amanda Lemos 100.0
Mayra Bueno Silva 100.0
Irene Aldana 100.0
Anthony Smith 100.0
Tim Elliott 100.0
Kelvin Gastelum 100.0
Jennifer Maia 100.0
Karolina Kowalkiewicz 100.0
Top ten fighters for Rounds_per_Fight:
FIGHTER Rounds_per_Fight
Demetrious Johnson 3.833333
Dominick Cruz 3.800000
Joanna Jedrzejczyk 3.800000
Valentina Shevchenko 3.750000
Israel Adesanya 3.750000
Benson Henderson 3.714286
Kamaru Usman 3.705882
Colby Covington 3.625000
Holly Holm 3.533333
Leon Edwards 3.529412
# Filter the dataframe using the correct column name "FIGHTER"
records_1 = records[records['FIGHTER'].isin(top_1_percent_list)]
records_1
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | Total_Rounds | Total_Fights | Wins | Losses | Draws | No_Contests | Win_by_KO_TKO | Win_by_Submission | Win_by_Decision | Loss_by_KO_TKO | Loss_by_Submission | Loss_by_Decision | Total_Fights_with_Title | Wins_with_Title | Losses_with_Title | Draws_with_Title | No_Contests_with_Title | Win_by_KO_TKO_with_Title | Win_by_Submission_with_Title | Win_by_Decision_with_Title | Loss_by_KO_TKO_with_Title | Loss_by_Submission_with_Title | Loss_by_Decision_with_Title | Win_% | Loss_% | KO_Win_% | Sub_Win_% | Dec_Win_% | KO_Loss_% | Sub_Loss_% | Dec_Loss_% | Title_Win_% | Title_Loss_% | Title_KO_Win_% | Title_Sub_Win_% | Title_Dec_Win_% | Title_KO_Loss_% | Title_Sub_Loss_% | Title_Dec_Loss_% | Rounds_per_Fight | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5 | Dustin Poirier | Southpaw | No | Yes | Yes | Yes | 71 | 30 | 22 | 7 | 0 | 1 | 11 | 4 | 7 | 3 | 3 | 1 | 3.0 | 1.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 2.0 | 0.0 | 73.333333 | 23.333333 | 36.666667 | 13.333333 | 23.333333 | 10.000000 | 10.000000 | 3.333333 | 33.333333 | 66.666667 | 0.000000 | 0.000000 | 33.333333 | 0.000000 | 66.666667 | 0.000000 | 2.366667 |
| 181 | Jim Miller | Southpaw | No | No | No | No | 96 | 43 | 26 | 16 | 0 | 1 | 6 | 12 | 8 | 2 | 3 | 11 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 60.465116 | 37.209302 | 13.953488 | 27.906977 | 18.604651 | 4.651163 | 6.976744 | 25.581395 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.232558 |
| 617 | Jon Jones | Orthodox | Yes | Yes | Yes | Yes | 74 | 23 | 21 | 1 | 0 | 1 | 5 | 6 | 10 | 0 | 0 | 0 | 16.0 | 15.0 | 0.0 | 0.0 | 1.0 | 3.0 | 4.0 | 8.0 | 0.0 | 0.0 | 0.0 | 91.304348 | 4.347826 | 21.739130 | 26.086957 | 43.478261 | 0.000000 | 0.000000 | 0.000000 | 93.750000 | 0.000000 | 18.750000 | 25.000000 | 50.000000 | 0.000000 | 0.000000 | 0.000000 | 3.217391 |
| 748 | Donald Cerrone | Orthodox | No | No | Yes | No | 81 | 38 | 23 | 14 | 0 | 1 | 10 | 6 | 7 | 8 | 1 | 5 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 60.526316 | 36.842105 | 26.315789 | 15.789474 | 18.421053 | 21.052632 | 2.631579 | 13.157895 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 2.131579 |
| 871 | Jeremy Stephens | Orthodox | No | No | No | No | 86 | 34 | 15 | 18 | 0 | 1 | 8 | 0 | 7 | 3 | 3 | 12 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 44.117647 | 52.941176 | 23.529412 | 0.000000 | 20.588235 | 8.823529 | 8.823529 | 35.294118 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.529412 |
| 874 | Conor McGregor | Southpaw | Yes | Yes | Yes | Yes | 28 | 14 | 10 | 4 | 0 | 0 | 8 | 0 | 2 | 2 | 2 | 0 | 4.0 | 3.0 | 1.0 | 0.0 | 0.0 | 3.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 71.428571 | 28.571429 | 57.142857 | 0.000000 | 14.285714 | 14.285714 | 14.285714 | 0.000000 | 75.000000 | 25.000000 | 75.000000 | 0.000000 | 0.000000 | 0.000000 | 25.000000 | 0.000000 | 2.000000 |
| 884 | Demian Maia | Southpaw | No | No | Yes | No | 89 | 33 | 22 | 11 | 0 | 0 | 1 | 11 | 10 | 2 | 0 | 9 | 2.0 | 0.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 66.666667 | 33.333333 | 3.030303 | 33.333333 | 30.303030 | 6.060606 | 0.000000 | 27.272727 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | 2.696970 |
| 946 | Anderson Silva | Southpaw | Yes | No | Yes | Yes | 63 | 25 | 17 | 7 | 0 | 1 | 11 | 3 | 3 | 4 | 0 | 3 | 13.0 | 11.0 | 2.0 | 0.0 | 0.0 | 7.0 | 2.0 | 2.0 | 2.0 | 0.0 | 0.0 | 68.000000 | 28.000000 | 44.000000 | 12.000000 | 12.000000 | 16.000000 | 0.000000 | 12.000000 | 84.615385 | 15.384615 | 53.846154 | 15.384615 | 15.384615 | 15.384615 | 0.000000 | 0.000000 | 2.520000 |
| 958 | Diego Sanchez | Southpaw | No | No | Yes | No | 84 | 32 | 19 | 13 | 0 | 0 | 6 | 0 | 12 | 4 | 0 | 9 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 59.375000 | 40.625000 | 18.750000 | 0.000000 | 37.500000 | 12.500000 | 0.000000 | 28.125000 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 2.625000 |
| 1261 | Johny Hendricks | Southpaw | Yes | No | Yes | Yes | 56 | 21 | 13 | 8 | 0 | 0 | 5 | 0 | 8 | 3 | 0 | 5 | 3.0 | 1.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 2.0 | 61.904762 | 38.095238 | 23.809524 | 0.000000 | 38.095238 | 14.285714 | 0.000000 | 23.809524 | 33.333333 | 66.666667 | 0.000000 | 0.000000 | 33.333333 | 0.000000 | 0.000000 | 66.666667 | 2.666667 |
| 1262 | Georges St-Pierre | Orthodox | Yes | Yes | Yes | Yes | 71 | 22 | 20 | 2 | 0 | 0 | 5 | 3 | 12 | 1 | 1 | 0 | 15.0 | 13.0 | 2.0 | 0.0 | 0.0 | 3.0 | 2.0 | 8.0 | 1.0 | 1.0 | 0.0 | 90.909091 | 9.090909 | 22.727273 | 13.636364 | 54.545455 | 4.545455 | 4.545455 | 0.000000 | 86.666667 | 13.333333 | 20.000000 | 13.333333 | 53.333333 | 6.666667 | 6.666667 | 0.000000 | 3.227273 |
| 1431 | Frank Mir | Southpaw | Yes | Yes | Yes | Yes | 43 | 27 | 16 | 11 | 0 | 0 | 5 | 8 | 2 | 8 | 0 | 3 | 5.0 | 2.0 | 3.0 | 0.0 | 0.0 | 1.0 | 1.0 | 0.0 | 3.0 | 0.0 | 0.0 | 59.259259 | 40.740741 | 18.518519 | 29.629630 | 7.407407 | 29.629630 | 0.000000 | 11.111111 | 40.000000 | 60.000000 | 20.000000 | 20.000000 | 0.000000 | 60.000000 | 0.000000 | 0.000000 | 1.592593 |
| 1785 | Jon Fitch | Orthodox | No | No | Yes | No | 50 | 18 | 14 | 3 | 1 | 0 | 1 | 3 | 10 | 1 | 0 | 2 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 77.777778 | 16.666667 | 5.555556 | 16.666667 | 55.555556 | 5.555556 | 0.000000 | 11.111111 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | 2.777778 |
records_1.describe()
| Total_Rounds | Total_Fights | Wins | Losses | Draws | No_Contests | Win_by_KO_TKO | Win_by_Submission | Win_by_Decision | Loss_by_KO_TKO | Loss_by_Submission | Loss_by_Decision | Total_Fights_with_Title | Wins_with_Title | Losses_with_Title | Draws_with_Title | No_Contests_with_Title | Win_by_KO_TKO_with_Title | Win_by_Submission_with_Title | Win_by_Decision_with_Title | Loss_by_KO_TKO_with_Title | Loss_by_Submission_with_Title | Loss_by_Decision_with_Title | Win_% | Loss_% | KO_Win_% | Sub_Win_% | Dec_Win_% | KO_Loss_% | Sub_Loss_% | Dec_Loss_% | Title_Win_% | Title_Loss_% | Title_KO_Win_% | Title_Sub_Win_% | Title_Dec_Win_% | Title_KO_Loss_% | Title_Sub_Loss_% | Title_Dec_Loss_% | Rounds_per_Fight | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.0 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 11.000000 | 11.000000 | 11.000000 | 11.000000 | 11.000000 | 11.000000 | 11.000000 | 11.000000 | 13.000000 |
| mean | 68.615385 | 27.692308 | 18.307692 | 8.846154 | 0.076923 | 0.461538 | 6.307692 | 4.307692 | 7.538462 | 3.153846 | 1.000000 | 4.615385 | 4.923077 | 3.538462 | 1.307692 | 0.0 | 0.076923 | 1.307692 | 0.692308 | 1.538462 | 0.615385 | 0.307692 | 0.384615 | 68.082145 | 29.984389 | 24.287578 | 14.490980 | 28.778303 | 11.337692 | 3.635617 | 14.676632 | 40.608974 | 58.822844 | 17.054196 | 6.701632 | 16.853147 | 25.641026 | 8.939394 | 24.242424 | 2.506453 |
| std | 19.876865 | 8.270274 | 4.589565 | 5.490084 | 0.277350 | 0.518875 | 3.250247 | 4.110649 | 3.430631 | 2.444250 | 1.290994 | 4.311582 | 5.780161 | 5.531958 | 0.947331 | 0.0 | 0.277350 | 2.136376 | 1.250641 | 2.933013 | 0.960769 | 0.630425 | 0.767948 | 13.154068 | 13.703613 | 14.744031 | 12.087589 | 15.858795 | 7.948917 | 4.885074 | 12.167930 | 38.428092 | 39.328052 | 25.449301 | 9.723341 | 21.631086 | 40.803243 | 20.578747 | 42.402592 | 0.456626 |
| min | 28.000000 | 14.000000 | 10.000000 | 1.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 44.117647 | 4.347826 | 3.030303 | 0.000000 | 7.407407 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.592593 |
| 25% | 56.000000 | 22.000000 | 15.000000 | 4.000000 | 0.000000 | 0.000000 | 5.000000 | 0.000000 | 7.000000 | 2.000000 | 0.000000 | 1.000000 | 1.000000 | 0.000000 | 1.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 60.465116 | 23.333333 | 18.518519 | 0.000000 | 18.421053 | 5.555556 | 0.000000 | 3.333333 | 0.000000 | 20.192308 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 2.232558 |
| 50% | 71.000000 | 27.000000 | 19.000000 | 8.000000 | 0.000000 | 0.000000 | 6.000000 | 3.000000 | 8.000000 | 3.000000 | 0.000000 | 3.000000 | 3.000000 | 1.000000 | 1.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 66.666667 | 33.333333 | 22.727273 | 13.636364 | 23.333333 | 10.000000 | 0.000000 | 12.000000 | 33.333333 | 66.666667 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 2.529412 |
| 75% | 84.000000 | 33.000000 | 22.000000 | 13.000000 | 0.000000 | 1.000000 | 8.000000 | 6.000000 | 10.000000 | 4.000000 | 2.000000 | 9.000000 | 5.000000 | 3.000000 | 2.000000 | 0.0 | 0.000000 | 3.000000 | 1.000000 | 1.000000 | 1.000000 | 0.000000 | 0.000000 | 73.333333 | 38.095238 | 26.315789 | 26.086957 | 38.095238 | 14.285714 | 6.976744 | 25.581395 | 79.807692 | 100.000000 | 20.000000 | 14.358974 | 33.333333 | 37.692308 | 3.333333 | 33.333333 | 2.696970 |
| max | 96.000000 | 43.000000 | 26.000000 | 18.000000 | 1.000000 | 1.000000 | 11.000000 | 12.000000 | 12.000000 | 8.000000 | 3.000000 | 12.000000 | 16.000000 | 15.000000 | 3.000000 | 0.0 | 1.000000 | 7.000000 | 4.000000 | 8.000000 | 3.000000 | 2.000000 | 2.000000 | 91.304348 | 52.941176 | 57.142857 | 33.333333 | 55.555556 | 29.629630 | 14.285714 | 35.294118 | 93.750000 | 100.000000 | 75.000000 | 25.000000 | 53.333333 | 100.000000 | 66.666667 | 100.000000 | 3.227273 |
# Calculate the mean for each of the specified columns
means_1 = records_1[metrics].mean()
# Rank the means from highest to lowest and plot again
sorted_means_1 = means_1.sort_values(ascending=False)
# Display max rows
pd.set_option('display.max_rows', None)
print(sorted_means_1)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)
Total_Rounds 68.615385 Win_% 68.082145 Title_Loss_% 58.822844 Title_Win_% 40.608974 Loss_% 29.984389 Dec_Win_% 28.778303 Total_Fights 27.692308 Title_KO_Loss_% 25.641026 KO_Win_% 24.287578 Title_Dec_Loss_% 24.242424 Wins 18.307692 Title_KO_Win_% 17.054196 Title_Dec_Win_% 16.853147 Dec_Loss_% 14.676632 Sub_Win_% 14.490980 KO_Loss_% 11.337692 Title_Sub_Loss_% 8.939394 Losses 8.846154 Win_by_Decision 7.538462 Title_Sub_Win_% 6.701632 Win_by_KO_TKO 6.307692 Total_Fights_with_Title 4.923077 Loss_by_Decision 4.615385 Win_by_Submission 4.307692 Sub_Loss_% 3.635617 Wins_with_Title 3.538462 Loss_by_KO_TKO 3.153846 Rounds_per_Fight 2.506453 Win_by_Decision_with_Title 1.538462 Win_by_KO_TKO_with_Title 1.307692 Losses_with_Title 1.307692 Loss_by_Submission 1.000000 Win_by_Submission_with_Title 0.692308 Loss_by_KO_TKO_with_Title 0.615385 No_Contests 0.461538 Loss_by_Decision_with_Title 0.384615 Loss_by_Submission_with_Title 0.307692 No_Contests_with_Title 0.076923 Draws 0.076923 Draws_with_Title 0.000000 dtype: float64
# Filter the dataframe using the correct column name "FIGHTER"
records_10 = records[records['FIGHTER'].isin(top_1_percent_list)]
records_10
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | Total_Rounds | Total_Fights | Wins | Losses | Draws | No_Contests | Win_by_KO_TKO | Win_by_Submission | Win_by_Decision | Loss_by_KO_TKO | Loss_by_Submission | Loss_by_Decision | Total_Fights_with_Title | Wins_with_Title | Losses_with_Title | Draws_with_Title | No_Contests_with_Title | Win_by_KO_TKO_with_Title | Win_by_Submission_with_Title | Win_by_Decision_with_Title | Loss_by_KO_TKO_with_Title | Loss_by_Submission_with_Title | Loss_by_Decision_with_Title | Win_% | Loss_% | KO_Win_% | Sub_Win_% | Dec_Win_% | KO_Loss_% | Sub_Loss_% | Dec_Loss_% | Title_Win_% | Title_Loss_% | Title_KO_Win_% | Title_Sub_Win_% | Title_Dec_Win_% | Title_KO_Loss_% | Title_Sub_Loss_% | Title_Dec_Loss_% | Rounds_per_Fight | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5 | Dustin Poirier | Southpaw | No | Yes | Yes | Yes | 71 | 30 | 22 | 7 | 0 | 1 | 11 | 4 | 7 | 3 | 3 | 1 | 3.0 | 1.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 2.0 | 0.0 | 73.333333 | 23.333333 | 36.666667 | 13.333333 | 23.333333 | 10.000000 | 10.000000 | 3.333333 | 33.333333 | 66.666667 | 0.000000 | 0.000000 | 33.333333 | 0.000000 | 66.666667 | 0.000000 | 2.366667 |
| 181 | Jim Miller | Southpaw | No | No | No | No | 96 | 43 | 26 | 16 | 0 | 1 | 6 | 12 | 8 | 2 | 3 | 11 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 60.465116 | 37.209302 | 13.953488 | 27.906977 | 18.604651 | 4.651163 | 6.976744 | 25.581395 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.232558 |
| 617 | Jon Jones | Orthodox | Yes | Yes | Yes | Yes | 74 | 23 | 21 | 1 | 0 | 1 | 5 | 6 | 10 | 0 | 0 | 0 | 16.0 | 15.0 | 0.0 | 0.0 | 1.0 | 3.0 | 4.0 | 8.0 | 0.0 | 0.0 | 0.0 | 91.304348 | 4.347826 | 21.739130 | 26.086957 | 43.478261 | 0.000000 | 0.000000 | 0.000000 | 93.750000 | 0.000000 | 18.750000 | 25.000000 | 50.000000 | 0.000000 | 0.000000 | 0.000000 | 3.217391 |
| 748 | Donald Cerrone | Orthodox | No | No | Yes | No | 81 | 38 | 23 | 14 | 0 | 1 | 10 | 6 | 7 | 8 | 1 | 5 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 60.526316 | 36.842105 | 26.315789 | 15.789474 | 18.421053 | 21.052632 | 2.631579 | 13.157895 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 2.131579 |
| 871 | Jeremy Stephens | Orthodox | No | No | No | No | 86 | 34 | 15 | 18 | 0 | 1 | 8 | 0 | 7 | 3 | 3 | 12 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 44.117647 | 52.941176 | 23.529412 | 0.000000 | 20.588235 | 8.823529 | 8.823529 | 35.294118 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.529412 |
| 874 | Conor McGregor | Southpaw | Yes | Yes | Yes | Yes | 28 | 14 | 10 | 4 | 0 | 0 | 8 | 0 | 2 | 2 | 2 | 0 | 4.0 | 3.0 | 1.0 | 0.0 | 0.0 | 3.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 71.428571 | 28.571429 | 57.142857 | 0.000000 | 14.285714 | 14.285714 | 14.285714 | 0.000000 | 75.000000 | 25.000000 | 75.000000 | 0.000000 | 0.000000 | 0.000000 | 25.000000 | 0.000000 | 2.000000 |
| 884 | Demian Maia | Southpaw | No | No | Yes | No | 89 | 33 | 22 | 11 | 0 | 0 | 1 | 11 | 10 | 2 | 0 | 9 | 2.0 | 0.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 66.666667 | 33.333333 | 3.030303 | 33.333333 | 30.303030 | 6.060606 | 0.000000 | 27.272727 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | 2.696970 |
| 946 | Anderson Silva | Southpaw | Yes | No | Yes | Yes | 63 | 25 | 17 | 7 | 0 | 1 | 11 | 3 | 3 | 4 | 0 | 3 | 13.0 | 11.0 | 2.0 | 0.0 | 0.0 | 7.0 | 2.0 | 2.0 | 2.0 | 0.0 | 0.0 | 68.000000 | 28.000000 | 44.000000 | 12.000000 | 12.000000 | 16.000000 | 0.000000 | 12.000000 | 84.615385 | 15.384615 | 53.846154 | 15.384615 | 15.384615 | 15.384615 | 0.000000 | 0.000000 | 2.520000 |
| 958 | Diego Sanchez | Southpaw | No | No | Yes | No | 84 | 32 | 19 | 13 | 0 | 0 | 6 | 0 | 12 | 4 | 0 | 9 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 59.375000 | 40.625000 | 18.750000 | 0.000000 | 37.500000 | 12.500000 | 0.000000 | 28.125000 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 2.625000 |
| 1261 | Johny Hendricks | Southpaw | Yes | No | Yes | Yes | 56 | 21 | 13 | 8 | 0 | 0 | 5 | 0 | 8 | 3 | 0 | 5 | 3.0 | 1.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 2.0 | 61.904762 | 38.095238 | 23.809524 | 0.000000 | 38.095238 | 14.285714 | 0.000000 | 23.809524 | 33.333333 | 66.666667 | 0.000000 | 0.000000 | 33.333333 | 0.000000 | 0.000000 | 66.666667 | 2.666667 |
| 1262 | Georges St-Pierre | Orthodox | Yes | Yes | Yes | Yes | 71 | 22 | 20 | 2 | 0 | 0 | 5 | 3 | 12 | 1 | 1 | 0 | 15.0 | 13.0 | 2.0 | 0.0 | 0.0 | 3.0 | 2.0 | 8.0 | 1.0 | 1.0 | 0.0 | 90.909091 | 9.090909 | 22.727273 | 13.636364 | 54.545455 | 4.545455 | 4.545455 | 0.000000 | 86.666667 | 13.333333 | 20.000000 | 13.333333 | 53.333333 | 6.666667 | 6.666667 | 0.000000 | 3.227273 |
| 1431 | Frank Mir | Southpaw | Yes | Yes | Yes | Yes | 43 | 27 | 16 | 11 | 0 | 0 | 5 | 8 | 2 | 8 | 0 | 3 | 5.0 | 2.0 | 3.0 | 0.0 | 0.0 | 1.0 | 1.0 | 0.0 | 3.0 | 0.0 | 0.0 | 59.259259 | 40.740741 | 18.518519 | 29.629630 | 7.407407 | 29.629630 | 0.000000 | 11.111111 | 40.000000 | 60.000000 | 20.000000 | 20.000000 | 0.000000 | 60.000000 | 0.000000 | 0.000000 | 1.592593 |
| 1785 | Jon Fitch | Orthodox | No | No | Yes | No | 50 | 18 | 14 | 3 | 1 | 0 | 1 | 3 | 10 | 1 | 0 | 2 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 77.777778 | 16.666667 | 5.555556 | 16.666667 | 55.555556 | 5.555556 | 0.000000 | 11.111111 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | 2.777778 |
records_10.describe()
| Total_Rounds | Total_Fights | Wins | Losses | Draws | No_Contests | Win_by_KO_TKO | Win_by_Submission | Win_by_Decision | Loss_by_KO_TKO | Loss_by_Submission | Loss_by_Decision | Total_Fights_with_Title | Wins_with_Title | Losses_with_Title | Draws_with_Title | No_Contests_with_Title | Win_by_KO_TKO_with_Title | Win_by_Submission_with_Title | Win_by_Decision_with_Title | Loss_by_KO_TKO_with_Title | Loss_by_Submission_with_Title | Loss_by_Decision_with_Title | Win_% | Loss_% | KO_Win_% | Sub_Win_% | Dec_Win_% | KO_Loss_% | Sub_Loss_% | Dec_Loss_% | Title_Win_% | Title_Loss_% | Title_KO_Win_% | Title_Sub_Win_% | Title_Dec_Win_% | Title_KO_Loss_% | Title_Sub_Loss_% | Title_Dec_Loss_% | Rounds_per_Fight | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.0 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 11.000000 | 11.000000 | 11.000000 | 11.000000 | 11.000000 | 11.000000 | 11.000000 | 11.000000 | 13.000000 |
| mean | 68.615385 | 27.692308 | 18.307692 | 8.846154 | 0.076923 | 0.461538 | 6.307692 | 4.307692 | 7.538462 | 3.153846 | 1.000000 | 4.615385 | 4.923077 | 3.538462 | 1.307692 | 0.0 | 0.076923 | 1.307692 | 0.692308 | 1.538462 | 0.615385 | 0.307692 | 0.384615 | 68.082145 | 29.984389 | 24.287578 | 14.490980 | 28.778303 | 11.337692 | 3.635617 | 14.676632 | 40.608974 | 58.822844 | 17.054196 | 6.701632 | 16.853147 | 25.641026 | 8.939394 | 24.242424 | 2.506453 |
| std | 19.876865 | 8.270274 | 4.589565 | 5.490084 | 0.277350 | 0.518875 | 3.250247 | 4.110649 | 3.430631 | 2.444250 | 1.290994 | 4.311582 | 5.780161 | 5.531958 | 0.947331 | 0.0 | 0.277350 | 2.136376 | 1.250641 | 2.933013 | 0.960769 | 0.630425 | 0.767948 | 13.154068 | 13.703613 | 14.744031 | 12.087589 | 15.858795 | 7.948917 | 4.885074 | 12.167930 | 38.428092 | 39.328052 | 25.449301 | 9.723341 | 21.631086 | 40.803243 | 20.578747 | 42.402592 | 0.456626 |
| min | 28.000000 | 14.000000 | 10.000000 | 1.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 44.117647 | 4.347826 | 3.030303 | 0.000000 | 7.407407 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.592593 |
| 25% | 56.000000 | 22.000000 | 15.000000 | 4.000000 | 0.000000 | 0.000000 | 5.000000 | 0.000000 | 7.000000 | 2.000000 | 0.000000 | 1.000000 | 1.000000 | 0.000000 | 1.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 60.465116 | 23.333333 | 18.518519 | 0.000000 | 18.421053 | 5.555556 | 0.000000 | 3.333333 | 0.000000 | 20.192308 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 2.232558 |
| 50% | 71.000000 | 27.000000 | 19.000000 | 8.000000 | 0.000000 | 0.000000 | 6.000000 | 3.000000 | 8.000000 | 3.000000 | 0.000000 | 3.000000 | 3.000000 | 1.000000 | 1.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 66.666667 | 33.333333 | 22.727273 | 13.636364 | 23.333333 | 10.000000 | 0.000000 | 12.000000 | 33.333333 | 66.666667 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 2.529412 |
| 75% | 84.000000 | 33.000000 | 22.000000 | 13.000000 | 0.000000 | 1.000000 | 8.000000 | 6.000000 | 10.000000 | 4.000000 | 2.000000 | 9.000000 | 5.000000 | 3.000000 | 2.000000 | 0.0 | 0.000000 | 3.000000 | 1.000000 | 1.000000 | 1.000000 | 0.000000 | 0.000000 | 73.333333 | 38.095238 | 26.315789 | 26.086957 | 38.095238 | 14.285714 | 6.976744 | 25.581395 | 79.807692 | 100.000000 | 20.000000 | 14.358974 | 33.333333 | 37.692308 | 3.333333 | 33.333333 | 2.696970 |
| max | 96.000000 | 43.000000 | 26.000000 | 18.000000 | 1.000000 | 1.000000 | 11.000000 | 12.000000 | 12.000000 | 8.000000 | 3.000000 | 12.000000 | 16.000000 | 15.000000 | 3.000000 | 0.0 | 1.000000 | 7.000000 | 4.000000 | 8.000000 | 3.000000 | 2.000000 | 2.000000 | 91.304348 | 52.941176 | 57.142857 | 33.333333 | 55.555556 | 29.629630 | 14.285714 | 35.294118 | 93.750000 | 100.000000 | 75.000000 | 25.000000 | 53.333333 | 100.000000 | 66.666667 | 100.000000 | 3.227273 |
# Calculate the mean for each of the specified columns
means_10 = records_10[metrics].mean()
# Rank the means from highest to lowest and plot again
sorted_means_10 = means_10.sort_values(ascending=False)
# Display max rows
pd.set_option('display.max_rows', None)
print(sorted_means_10)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)
Total_Rounds 68.615385 Win_% 68.082145 Title_Loss_% 58.822844 Title_Win_% 40.608974 Loss_% 29.984389 Dec_Win_% 28.778303 Total_Fights 27.692308 Title_KO_Loss_% 25.641026 KO_Win_% 24.287578 Title_Dec_Loss_% 24.242424 Wins 18.307692 Title_KO_Win_% 17.054196 Title_Dec_Win_% 16.853147 Dec_Loss_% 14.676632 Sub_Win_% 14.490980 KO_Loss_% 11.337692 Title_Sub_Loss_% 8.939394 Losses 8.846154 Win_by_Decision 7.538462 Title_Sub_Win_% 6.701632 Win_by_KO_TKO 6.307692 Total_Fights_with_Title 4.923077 Loss_by_Decision 4.615385 Win_by_Submission 4.307692 Sub_Loss_% 3.635617 Wins_with_Title 3.538462 Loss_by_KO_TKO 3.153846 Rounds_per_Fight 2.506453 Win_by_Decision_with_Title 1.538462 Win_by_KO_TKO_with_Title 1.307692 Losses_with_Title 1.307692 Loss_by_Submission 1.000000 Win_by_Submission_with_Title 0.692308 Loss_by_KO_TKO_with_Title 0.615385 No_Contests 0.461538 Loss_by_Decision_with_Title 0.384615 Loss_by_Submission_with_Title 0.307692 No_Contests_with_Title 0.076923 Draws 0.076923 Draws_with_Title 0.000000 dtype: float64
# Bottom 90 Percent
records_bottom_90 = records[~records['FIGHTER'].isin(top_1_percent_list)]
records_bottom_90
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | Total_Rounds | Total_Fights | Wins | Losses | Draws | No_Contests | Win_by_KO_TKO | Win_by_Submission | Win_by_Decision | Loss_by_KO_TKO | Loss_by_Submission | Loss_by_Decision | Total_Fights_with_Title | Wins_with_Title | Losses_with_Title | Draws_with_Title | No_Contests_with_Title | Win_by_KO_TKO_with_Title | Win_by_Submission_with_Title | Win_by_Decision_with_Title | Loss_by_KO_TKO_with_Title | Loss_by_Submission_with_Title | Loss_by_Decision_with_Title | Win_% | Loss_% | KO_Win_% | Sub_Win_% | Dec_Win_% | KO_Loss_% | Sub_Loss_% | Dec_Loss_% | Title_Win_% | Title_Loss_% | Title_KO_Win_% | Title_Sub_Win_% | Title_Dec_Win_% | Title_KO_Loss_% | Title_Sub_Loss_% | Title_Dec_Loss_% | Rounds_per_Fight | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Asu Almabayev | None | No | No | No | No | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 3.000000 |
| 1 | CJ Vergara | Orthodox | No | No | No | No | 16 | 6 | 3 | 3 | 0 | 0 | 1 | 0 | 2 | 0 | 1 | 2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 50.000000 | 50.000000 | 16.666667 | 0.000000 | 33.333333 | 0.000000 | 16.666667 | 33.333333 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.666667 |
| 2 | Curtis Blaydes | Orthodox | No | No | No | No | 41 | 18 | 13 | 4 | 0 | 1 | 8 | 0 | 5 | 4 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 72.222222 | 22.222222 | 44.444444 | 0.000000 | 27.777778 | 22.222222 | 0.000000 | 0.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.277778 |
| 3 | Jailton Almeida | Orthodox | No | No | No | No | 13 | 7 | 6 | 1 | 0 | 0 | 2 | 3 | 1 | 1 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 85.714286 | 14.285714 | 28.571429 | 42.857143 | 14.285714 | 14.285714 | 0.000000 | 0.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.857143 |
| 4 | Benoit Saint Denis | Southpaw | No | No | No | No | 13 | 7 | 5 | 2 | 0 | 0 | 3 | 2 | 0 | 1 | 0 | 1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 71.428571 | 28.571429 | 42.857143 | 28.571429 | 0.000000 | 14.285714 | 0.000000 | 14.285714 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.857143 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2312 | Joao Roque | Orthodox | No | No | No | No | 3 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 3.000000 |
| 2313 | Marcelo Aguiar | Orthodox | No | No | No | No | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.000000 |
| 2314 | Adrian Serrano | None | No | No | No | No | 2 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.000000 |
| 2315 | David Dodd | Orthodox | No | No | No | No | 3 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 3.000000 |
| 2316 | Tyrone Roberts | Orthodox | No | No | No | No | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 3.000000 |
2304 rows × 46 columns
records_bottom_90.describe()
| Total_Rounds | Total_Fights | Wins | Losses | Draws | No_Contests | Win_by_KO_TKO | Win_by_Submission | Win_by_Decision | Loss_by_KO_TKO | Loss_by_Submission | Loss_by_Decision | Total_Fights_with_Title | Wins_with_Title | Losses_with_Title | Draws_with_Title | No_Contests_with_Title | Win_by_KO_TKO_with_Title | Win_by_Submission_with_Title | Win_by_Decision_with_Title | Loss_by_KO_TKO_with_Title | Loss_by_Submission_with_Title | Loss_by_Decision_with_Title | Win_% | Loss_% | KO_Win_% | Sub_Win_% | Dec_Win_% | KO_Loss_% | Sub_Loss_% | Dec_Loss_% | Title_Win_% | Title_Loss_% | Title_KO_Win_% | Title_Sub_Win_% | Title_Dec_Win_% | Title_KO_Loss_% | Title_Sub_Loss_% | Title_Dec_Loss_% | Rounds_per_Fight | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 2304.000000 | 2304.000000 | 2304.000000 | 2304.000000 | 2304.000000 | 2304.000000 | 2304.000000 | 2304.000000 | 2304.000000 | 2304.000000 | 2304.000000 | 2304.000000 | 2304.000000 | 2304.000000 | 2304.000000 | 2304.000000 | 2304.000000 | 2304.000000 | 2304.000000 | 2304.000000 | 2304.000000 | 2304.000000 | 2304.000000 | 2304.000000 | 2304.000000 | 2304.000000 | 2304.000000 | 2304.000000 | 2304.000000 | 2304.000000 | 2304.000000 | 210.000000 | 210.000000 | 210.000000 | 210.000000 | 210.000000 | 210.000000 | 210.000000 | 210.000000 | 2304.000000 |
| mean | 14.623264 | 6.171875 | 3.003472 | 3.057726 | 0.046441 | 0.064236 | 0.994358 | 0.583767 | 1.417101 | 1.013889 | 0.601997 | 1.433160 | 0.262153 | 0.121962 | 0.134549 | 0.005208 | 0.000434 | 0.053819 | 0.021267 | 0.046441 | 0.057726 | 0.023438 | 0.052951 | 36.323768 | 61.847029 | 11.652643 | 7.121023 | 17.380853 | 21.405818 | 13.572657 | 26.656872 | 28.217670 | 69.809507 | 11.886020 | 5.268759 | 10.967653 | 29.201299 | 13.230365 | 27.258795 | 2.269684 |
| std | 14.554776 | 5.640170 | 3.706912 | 2.331357 | 0.220557 | 0.255630 | 1.705006 | 1.192288 | 2.041490 | 1.223513 | 0.923949 | 1.584459 | 1.173906 | 0.755564 | 0.518142 | 0.071996 | 0.020833 | 0.374691 | 0.214496 | 0.356096 | 0.298587 | 0.170227 | 0.291387 | 29.225672 | 29.728415 | 17.879819 | 13.620865 | 21.137844 | 28.102144 | 23.956558 | 28.299674 | 33.643181 | 34.679223 | 20.683441 | 14.025604 | 21.721206 | 37.584210 | 29.789935 | 38.165220 | 0.599040 |
| min | 1.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 |
| 25% | 4.000000 | 2.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 40.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 40.714286 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 2.000000 |
| 50% | 9.000000 | 4.000000 | 2.000000 | 2.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 37.500000 | 60.000000 | 0.000000 | 0.000000 | 11.111111 | 11.437908 | 0.000000 | 20.000000 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | 9.545455 | 0.000000 | 0.000000 | 2.333333 |
| 75% | 20.000000 | 8.000000 | 4.000000 | 4.000000 | 0.000000 | 0.000000 | 1.000000 | 1.000000 | 2.000000 | 1.250000 | 1.000000 | 2.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 57.142857 | 100.000000 | 20.000000 | 11.111111 | 31.250000 | 33.333333 | 20.000000 | 42.857143 | 50.000000 | 100.000000 | 20.000000 | 0.000000 | 16.666667 | 50.000000 | 0.000000 | 50.000000 | 2.724922 |
| max | 111.000000 | 41.000000 | 23.000000 | 18.000000 | 2.000000 | 2.000000 | 14.000000 | 16.000000 | 14.000000 | 8.000000 | 7.000000 | 11.000000 | 14.000000 | 12.000000 | 6.000000 | 1.000000 | 1.000000 | 5.000000 | 5.000000 | 6.000000 | 6.000000 | 3.000000 | 5.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 75.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 4.000000 |
# Calculate the mean for each of the specified columns
means_90 = records_bottom_90[metrics].mean()
# Rank the means from highest to lowest and plot again
sorted_means_90 = means_90.sort_values(ascending=False)
# Display max rows
pd.set_option('display.max_rows', None)
print(sorted_means_90)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)
Title_Loss_% 69.809507 Loss_% 61.847029 Win_% 36.323768 Title_KO_Loss_% 29.201299 Title_Win_% 28.217670 Title_Dec_Loss_% 27.258795 Dec_Loss_% 26.656872 KO_Loss_% 21.405818 Dec_Win_% 17.380853 Total_Rounds 14.623264 Sub_Loss_% 13.572657 Title_Sub_Loss_% 13.230365 Title_KO_Win_% 11.886020 KO_Win_% 11.652643 Title_Dec_Win_% 10.967653 Sub_Win_% 7.121023 Total_Fights 6.171875 Title_Sub_Win_% 5.268759 Losses 3.057726 Wins 3.003472 Rounds_per_Fight 2.269684 Loss_by_Decision 1.433160 Win_by_Decision 1.417101 Loss_by_KO_TKO 1.013889 Win_by_KO_TKO 0.994358 Loss_by_Submission 0.601997 Win_by_Submission 0.583767 Total_Fights_with_Title 0.262153 Losses_with_Title 0.134549 Wins_with_Title 0.121962 No_Contests 0.064236 Loss_by_KO_TKO_with_Title 0.057726 Win_by_KO_TKO_with_Title 0.053819 Loss_by_Decision_with_Title 0.052951 Win_by_Decision_with_Title 0.046441 Draws 0.046441 Loss_by_Submission_with_Title 0.023438 Win_by_Submission_with_Title 0.021267 Draws_with_Title 0.005208 No_Contests_with_Title 0.000434 dtype: float64
# Champions
r_champions = r[r['EITHER_CHAMP'] == 'Yes']
r_champions
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | Total_Rounds | Total_Fights | Wins | Losses | Draws | No_Contests | Win_by_KO_TKO | Win_by_Submission | Win_by_Decision | Loss_by_KO_TKO | Loss_by_Submission | Loss_by_Decision | Total_Fights_with_Title | Wins_with_Title | Losses_with_Title | Draws_with_Title | No_Contests_with_Title | Win_by_KO_TKO_with_Title | Win_by_Submission_with_Title | Win_by_Decision_with_Title | Loss_by_KO_TKO_with_Title | Loss_by_Submission_with_Title | Loss_by_Decision_with_Title | Win_% | Loss_% | KO_Win_% | Sub_Win_% | Dec_Win_% | KO_Loss_% | Sub_Loss_% | Dec_Loss_% | Title_Win_% | Title_Loss_% | Title_KO_Win_% | Title_Sub_Win_% | Title_Dec_Win_% | Title_KO_Loss_% | Title_Sub_Loss_% | Title_Dec_Loss_% | Rounds_per_Fight | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5 | Dustin Poirier | Southpaw | No | Yes | Yes | Yes | 71 | 30 | 22 | 7 | 0 | 1 | 11 | 4 | 7 | 3 | 3 | 1 | 3.0 | 1.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 2.0 | 0.0 | 73.333333 | 23.333333 | 36.666667 | 13.333333 | 23.333333 | 10.000000 | 10.000000 | 3.333333 | 33.333333 | 66.666667 | 0.000000 | 0.000000 | 33.333333 | 0.000000 | 66.666667 | 0.000000 | 2.366667 |
| 17 | Rafael Dos Anjos | Southpaw | Yes | No | Yes | Yes | 111 | 35 | 21 | 14 | 0 | 0 | 4 | 5 | 12 | 3 | 1 | 10 | 4.0 | 2.0 | 2.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 | 1.0 | 0.0 | 1.0 | 60.000000 | 40.000000 | 11.428571 | 14.285714 | 34.285714 | 8.571429 | 2.857143 | 28.571429 | 50.000000 | 50.000000 | 25.000000 | 0.000000 | 25.000000 | 25.000000 | 0.000000 | 25.000000 | 3.171429 |
| 22 | Petr Yan | Switch | Yes | Yes | Yes | Yes | 45 | 13 | 9 | 4 | 0 | 0 | 4 | 0 | 5 | 0 | 0 | 3 | 4.0 | 2.0 | 2.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 1.0 | 69.230769 | 30.769231 | 30.769231 | 0.000000 | 38.461538 | 0.000000 | 0.000000 | 23.076923 | 50.000000 | 50.000000 | 25.000000 | 0.000000 | 25.000000 | 0.000000 | 0.000000 | 25.000000 | 3.461538 |
| 27 | Sean O'Malley | Switch | Yes | No | Yes | Yes | 28 | 12 | 10 | 1 | 0 | 1 | 6 | 0 | 4 | 1 | 0 | 0 | 2.0 | 2.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 83.333333 | 8.333333 | 50.000000 | 0.000000 | 33.333333 | 8.333333 | 0.000000 | 0.000000 | 100.000000 | 0.000000 | 50.000000 | 0.000000 | 50.000000 | 0.000000 | 0.000000 | 0.000000 | 2.333333 |
| 50 | Brandon Moreno | Orthodox | Yes | Yes | Yes | Yes | 53 | 16 | 9 | 5 | 2 | 0 | 3 | 3 | 3 | 0 | 0 | 5 | 6.0 | 3.0 | 2.0 | 1.0 | 0.0 | 2.0 | 1.0 | 0.0 | 0.0 | 0.0 | 2.0 | 56.250000 | 31.250000 | 18.750000 | 18.750000 | 18.750000 | 0.000000 | 0.000000 | 31.250000 | 50.000000 | 33.333333 | 33.333333 | 16.666667 | 0.000000 | 0.000000 | 0.000000 | 33.333333 | 3.312500 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2165 | Dave Menne | Orthodox | Yes | No | Yes | Yes | 12 | 5 | 1 | 4 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 1 | 2.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 20.000000 | 80.000000 | 0.000000 | 0.000000 | 20.000000 | 60.000000 | 0.000000 | 20.000000 | 50.000000 | 50.000000 | 0.000000 | 0.000000 | 50.000000 | 50.000000 | 0.000000 | 0.000000 | 2.400000 |
| 2241 | Carlos Newton | Orthodox | Yes | No | Yes | Yes | 13 | 5 | 2 | 3 | 0 | 0 | 0 | 2 | 0 | 2 | 0 | 1 | 3.0 | 1.0 | 2.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 2.0 | 0.0 | 0.0 | 40.000000 | 60.000000 | 0.000000 | 40.000000 | 0.000000 | 40.000000 | 0.000000 | 20.000000 | 33.333333 | 66.666667 | 0.000000 | 33.333333 | 0.000000 | 66.666667 | 0.000000 | 0.000000 | 2.600000 |
| 2246 | Ricco Rodriguez | Orthodox | Yes | No | Yes | Yes | 19 | 7 | 5 | 2 | 0 | 0 | 5 | 0 | 0 | 1 | 0 | 1 | 2.0 | 1.0 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 71.428571 | 28.571429 | 71.428571 | 0.000000 | 0.000000 | 14.285714 | 0.000000 | 14.285714 | 50.000000 | 50.000000 | 50.000000 | 0.000000 | 0.000000 | 50.000000 | 0.000000 | 0.000000 | 2.714286 |
| 2278 | Pat Miletich | Orthodox | Yes | No | Yes | Yes | 10 | 5 | 3 | 2 | 0 | 0 | 1 | 2 | 0 | 1 | 1 | 0 | 3.0 | 2.0 | 1.0 | 0.0 | 0.0 | 0.0 | 2.0 | 0.0 | 0.0 | 1.0 | 0.0 | 60.000000 | 40.000000 | 20.000000 | 40.000000 | 0.000000 | 20.000000 | 20.000000 | 0.000000 | 66.666667 | 33.333333 | 0.000000 | 66.666667 | 0.000000 | 0.000000 | 33.333333 | 0.000000 | 2.000000 |
| 2282 | Kevin Randleman | Orthodox | Yes | No | Yes | Yes | 12 | 4 | 2 | 2 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 2.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 50.000000 | 50.000000 | 0.000000 | 0.000000 | 50.000000 | 50.000000 | 0.000000 | 0.000000 | 50.000000 | 50.000000 | 0.000000 | 0.000000 | 50.000000 | 50.000000 | 0.000000 | 0.000000 | 3.000000 |
103 rows × 46 columns
r_champions.describe()
| Total_Rounds | Total_Fights | Wins | Losses | Draws | No_Contests | Win_by_KO_TKO | Win_by_Submission | Win_by_Decision | Loss_by_KO_TKO | Loss_by_Submission | Loss_by_Decision | Total_Fights_with_Title | Wins_with_Title | Losses_with_Title | Draws_with_Title | No_Contests_with_Title | Win_by_KO_TKO_with_Title | Win_by_Submission_with_Title | Win_by_Decision_with_Title | Loss_by_KO_TKO_with_Title | Loss_by_Submission_with_Title | Loss_by_Decision_with_Title | Win_% | Loss_% | KO_Win_% | Sub_Win_% | Dec_Win_% | KO_Loss_% | Sub_Loss_% | Dec_Loss_% | Title_Win_% | Title_Loss_% | Title_KO_Win_% | Title_Sub_Win_% | Title_Dec_Win_% | Title_KO_Loss_% | Title_Sub_Loss_% | Title_Dec_Loss_% | Rounds_per_Fight | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.00000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 |
| mean | 44.213592 | 16.281553 | 11.048544 | 4.970874 | 0.135922 | 0.126214 | 4.388350 | 1.893204 | 4.747573 | 2.126214 | 0.699029 | 2.126214 | 5.009709 | 3.145631 | 1.766990 | 0.07767 | 0.019417 | 1.359223 | 0.553398 | 1.223301 | 0.844660 | 0.291262 | 0.621359 | 68.529475 | 29.899001 | 28.092761 | 11.631376 | 28.718281 | 13.559472 | 4.239454 | 11.983180 | 59.926305 | 38.417939 | 25.569518 | 10.972401 | 23.190211 | 18.990201 | 6.731812 | 12.453208 | 2.694256 |
| std | 20.954503 | 7.100701 | 4.765848 | 3.270355 | 0.397259 | 0.333714 | 2.719692 | 2.300523 | 3.139622 | 1.877000 | 0.998381 | 2.003329 | 3.462673 | 2.894917 | 1.198069 | 0.26896 | 0.138662 | 1.454232 | 0.987452 | 1.644552 | 0.967687 | 0.571051 | 0.930070 | 13.587318 | 13.441360 | 16.901302 | 11.849880 | 16.191080 | 11.346138 | 5.935712 | 9.536207 | 21.417125 | 21.865765 | 24.033066 | 18.117930 | 25.491374 | 20.576808 | 14.258026 | 18.071435 | 0.575128 |
| min | 9.000000 | 4.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 1.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 20.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 20.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.125000 |
| 25% | 28.000000 | 12.000000 | 8.000000 | 2.000000 | 0.000000 | 0.000000 | 2.000000 | 0.000000 | 2.000000 | 1.000000 | 0.000000 | 1.000000 | 3.000000 | 1.000000 | 1.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 60.000000 | 21.428571 | 16.025641 | 0.000000 | 18.465909 | 6.250000 | 0.000000 | 4.673913 | 50.000000 | 25.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 2.345238 |
| 50% | 44.000000 | 15.000000 | 11.000000 | 4.000000 | 0.000000 | 0.000000 | 4.000000 | 1.000000 | 4.000000 | 2.000000 | 0.000000 | 2.000000 | 4.000000 | 2.000000 | 2.000000 | 0.00000 | 0.000000 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 0.000000 | 0.000000 | 69.230769 | 30.000000 | 25.000000 | 9.523810 | 28.571429 | 12.500000 | 0.000000 | 11.111111 | 57.142857 | 40.000000 | 25.000000 | 0.000000 | 20.000000 | 15.384615 | 0.000000 | 0.000000 | 2.666667 |
| 75% | 58.000000 | 20.000000 | 14.000000 | 7.000000 | 0.000000 | 0.000000 | 6.000000 | 3.000000 | 7.000000 | 3.000000 | 1.000000 | 3.000000 | 6.000000 | 4.000000 | 2.000000 | 0.00000 | 0.000000 | 2.000000 | 1.000000 | 1.500000 | 1.000000 | 0.000000 | 1.000000 | 75.595238 | 37.797619 | 37.980769 | 19.375000 | 39.230769 | 18.347339 | 7.142857 | 19.523810 | 75.000000 | 50.000000 | 38.750000 | 18.333333 | 36.931818 | 33.333333 | 0.000000 | 20.000000 | 3.000000 |
| max | 111.000000 | 41.000000 | 23.000000 | 17.000000 | 2.000000 | 1.000000 | 11.000000 | 16.000000 | 12.000000 | 8.000000 | 4.000000 | 10.000000 | 16.000000 | 15.000000 | 6.000000 | 1.00000 | 1.000000 | 7.000000 | 5.000000 | 8.000000 | 6.000000 | 3.000000 | 5.000000 | 100.000000 | 80.000000 | 71.428571 | 50.000000 | 64.285714 | 60.000000 | 23.076923 | 35.714286 | 100.000000 | 80.000000 | 100.000000 | 75.000000 | 100.000000 | 66.666667 | 66.666667 | 66.666667 | 3.833333 |
# Calculate the mean for each of the specified columns
means1 = r_champions[metrics].mean()
# Rank the means from highest to lowest and plot again
sorted_means1 = means1.sort_values(ascending=False)
# Display max rows
pd.set_option('display.max_rows', None)
print(sorted_means1)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)
Win_% 68.529475 Title_Win_% 59.926305 Total_Rounds 44.213592 Title_Loss_% 38.417939 Loss_% 29.899001 Dec_Win_% 28.718281 KO_Win_% 28.092761 Title_KO_Win_% 25.569518 Title_Dec_Win_% 23.190211 Title_KO_Loss_% 18.990201 Total_Fights 16.281553 KO_Loss_% 13.559472 Title_Dec_Loss_% 12.453208 Dec_Loss_% 11.983180 Sub_Win_% 11.631376 Wins 11.048544 Title_Sub_Win_% 10.972401 Title_Sub_Loss_% 6.731812 Total_Fights_with_Title 5.009709 Losses 4.970874 Win_by_Decision 4.747573 Win_by_KO_TKO 4.388350 Sub_Loss_% 4.239454 Wins_with_Title 3.145631 Rounds_per_Fight 2.694256 Loss_by_Decision 2.126214 Loss_by_KO_TKO 2.126214 Win_by_Submission 1.893204 Losses_with_Title 1.766990 Win_by_KO_TKO_with_Title 1.359223 Win_by_Decision_with_Title 1.223301 Loss_by_KO_TKO_with_Title 0.844660 Loss_by_Submission 0.699029 Loss_by_Decision_with_Title 0.621359 Win_by_Submission_with_Title 0.553398 Loss_by_Submission_with_Title 0.291262 Draws 0.135922 No_Contests 0.126214 Draws_with_Title 0.077670 No_Contests_with_Title 0.019417 dtype: float64
# Challengers
r_challengers = r[r['EVER_CHALLENGER'] == 'Yes']
r_challengers
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | Total_Rounds | Total_Fights | Wins | Losses | Draws | No_Contests | Win_by_KO_TKO | Win_by_Submission | Win_by_Decision | Loss_by_KO_TKO | Loss_by_Submission | Loss_by_Decision | Total_Fights_with_Title | Wins_with_Title | Losses_with_Title | Draws_with_Title | No_Contests_with_Title | Win_by_KO_TKO_with_Title | Win_by_Submission_with_Title | Win_by_Decision_with_Title | Loss_by_KO_TKO_with_Title | Loss_by_Submission_with_Title | Loss_by_Decision_with_Title | Win_% | Loss_% | KO_Win_% | Sub_Win_% | Dec_Win_% | KO_Loss_% | Sub_Loss_% | Dec_Loss_% | Title_Win_% | Title_Loss_% | Title_KO_Win_% | Title_Sub_Win_% | Title_Dec_Win_% | Title_KO_Loss_% | Title_Sub_Loss_% | Title_Dec_Loss_% | Rounds_per_Fight | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5 | Dustin Poirier | Southpaw | No | Yes | Yes | Yes | 71 | 30 | 22 | 7 | 0 | 1 | 11 | 4 | 7 | 3 | 3 | 1 | 3.0 | 1.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 2.0 | 0.0 | 73.333333 | 23.333333 | 36.666667 | 13.333333 | 23.333333 | 10.000000 | 10.000000 | 3.333333 | 33.333333 | 66.666667 | 0.0 | 0.000000 | 33.333333 | 0.000000 | 66.666667 | 0.000000 | 2.366667 |
| 6 | Gilbert Burns | Orthodox | No | No | Yes | No | 57 | 22 | 15 | 7 | 0 | 0 | 3 | 5 | 7 | 3 | 0 | 4 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 68.181818 | 31.818182 | 13.636364 | 22.727273 | 31.818182 | 13.636364 | 0.000000 | 18.181818 | 0.000000 | 100.000000 | 0.0 | 0.000000 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 2.590909 |
| 17 | Rafael Dos Anjos | Southpaw | Yes | No | Yes | Yes | 111 | 35 | 21 | 14 | 0 | 0 | 4 | 5 | 12 | 3 | 1 | 10 | 4.0 | 2.0 | 2.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 | 1.0 | 0.0 | 1.0 | 60.000000 | 40.000000 | 11.428571 | 14.285714 | 34.285714 | 8.571429 | 2.857143 | 28.571429 | 50.000000 | 50.000000 | 25.0 | 0.000000 | 25.000000 | 25.000000 | 0.000000 | 25.000000 | 3.171429 |
| 22 | Petr Yan | Switch | Yes | Yes | Yes | Yes | 45 | 13 | 9 | 4 | 0 | 0 | 4 | 0 | 5 | 0 | 0 | 3 | 4.0 | 2.0 | 2.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 1.0 | 69.230769 | 30.769231 | 30.769231 | 0.000000 | 38.461538 | 0.000000 | 0.000000 | 23.076923 | 50.000000 | 50.000000 | 25.0 | 0.000000 | 25.000000 | 0.000000 | 0.000000 | 25.000000 | 3.461538 |
| 26 | Marlon Vera | Switch | No | No | Yes | No | 66 | 23 | 15 | 8 | 0 | 0 | 7 | 4 | 4 | 0 | 0 | 8 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 65.217391 | 34.782609 | 30.434783 | 17.391304 | 17.391304 | 0.000000 | 0.000000 | 34.782609 | 0.000000 | 100.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | 2.869565 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2245 | Pedro Rizzo | Orthodox | No | No | Yes | No | 28 | 10 | 5 | 5 | 0 | 0 | 4 | 0 | 1 | 2 | 0 | 3 | 3.0 | 0.0 | 3.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 2.0 | 50.000000 | 50.000000 | 40.000000 | 0.000000 | 10.000000 | 20.000000 | 0.000000 | 30.000000 | 0.000000 | 100.000000 | 0.0 | 0.000000 | 0.000000 | 33.333333 | 0.000000 | 66.666667 | 2.800000 |
| 2246 | Ricco Rodriguez | Orthodox | Yes | No | Yes | Yes | 19 | 7 | 5 | 2 | 0 | 0 | 5 | 0 | 0 | 1 | 0 | 1 | 2.0 | 1.0 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 71.428571 | 28.571429 | 71.428571 | 0.000000 | 0.000000 | 14.285714 | 0.000000 | 14.285714 | 50.000000 | 50.000000 | 50.0 | 0.000000 | 0.000000 | 50.000000 | 0.000000 | 0.000000 | 2.714286 |
| 2251 | Gan McGee | Orthodox | No | No | Yes | No | 5 | 4 | 2 | 2 | 0 | 0 | 2 | 0 | 0 | 2 | 0 | 0 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 50.000000 | 50.000000 | 50.000000 | 0.000000 | 0.000000 | 50.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | 0.0 | 0.000000 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 1.250000 |
| 2278 | Pat Miletich | Orthodox | Yes | No | Yes | Yes | 10 | 5 | 3 | 2 | 0 | 0 | 1 | 2 | 0 | 1 | 1 | 0 | 3.0 | 2.0 | 1.0 | 0.0 | 0.0 | 0.0 | 2.0 | 0.0 | 0.0 | 1.0 | 0.0 | 60.000000 | 40.000000 | 20.000000 | 40.000000 | 0.000000 | 20.000000 | 20.000000 | 0.000000 | 66.666667 | 33.333333 | 0.0 | 66.666667 | 0.000000 | 0.000000 | 33.333333 | 0.000000 | 2.000000 |
| 2282 | Kevin Randleman | Orthodox | Yes | No | Yes | Yes | 12 | 4 | 2 | 2 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 2.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 50.000000 | 50.000000 | 0.000000 | 0.000000 | 50.000000 | 50.000000 | 0.000000 | 0.000000 | 50.000000 | 50.000000 | 0.0 | 0.000000 | 50.000000 | 50.000000 | 0.000000 | 0.000000 | 3.000000 |
213 rows × 46 columns
r_challengers.describe()
| Total_Rounds | Total_Fights | Wins | Losses | Draws | No_Contests | Win_by_KO_TKO | Win_by_Submission | Win_by_Decision | Loss_by_KO_TKO | Loss_by_Submission | Loss_by_Decision | Total_Fights_with_Title | Wins_with_Title | Losses_with_Title | Draws_with_Title | No_Contests_with_Title | Win_by_KO_TKO_with_Title | Win_by_Submission_with_Title | Win_by_Decision_with_Title | Loss_by_KO_TKO_with_Title | Loss_by_Submission_with_Title | Loss_by_Decision_with_Title | Win_% | Loss_% | KO_Win_% | Sub_Win_% | Dec_Win_% | KO_Loss_% | Sub_Loss_% | Dec_Loss_% | Title_Win_% | Title_Loss_% | Title_KO_Win_% | Title_Sub_Win_% | Title_Dec_Win_% | Title_KO_Loss_% | Title_Sub_Loss_% | Title_Dec_Loss_% | Rounds_per_Fight | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 |
| mean | 39.657277 | 15.150235 | 9.469484 | 5.460094 | 0.122066 | 0.098592 | 3.638498 | 1.676056 | 4.131455 | 2.037559 | 0.812207 | 2.600939 | 3.089202 | 1.521127 | 1.502347 | 0.056338 | 0.009390 | 0.657277 | 0.267606 | 0.591549 | 0.643192 | 0.262911 | 0.591549 | 61.308590 | 37.258460 | 24.026060 | 10.679770 | 26.484512 | 14.517436 | 5.639074 | 17.045424 | 28.978448 | 69.047172 | 12.364603 | 5.305903 | 11.214045 | 28.471005 | 12.566713 | 27.892083 | 2.583171 |
| std | 19.794460 | 6.967984 | 4.881159 | 3.050604 | 0.368745 | 0.298815 | 2.836059 | 2.174735 | 2.955902 | 1.845059 | 1.108476 | 2.247724 | 3.072478 | 2.552427 | 0.979143 | 0.231116 | 0.096672 | 1.216981 | 0.738901 | 1.294883 | 0.803538 | 0.519783 | 0.799365 | 15.596605 | 15.593465 | 16.740287 | 11.575076 | 16.264009 | 13.644809 | 7.905501 | 14.018623 | 33.492435 | 34.532522 | 21.022367 | 13.716553 | 21.156069 | 37.177658 | 28.624965 | 38.650714 | 0.546618 |
| min | 4.000000 | 4.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 |
| 25% | 23.000000 | 10.000000 | 6.000000 | 3.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 2.000000 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 53.846154 | 28.571429 | 12.500000 | 0.000000 | 14.285714 | 5.882353 | 0.000000 | 7.142857 | 0.000000 | 40.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 2.250000 |
| 50% | 38.000000 | 14.000000 | 9.000000 | 5.000000 | 0.000000 | 0.000000 | 3.000000 | 1.000000 | 4.000000 | 2.000000 | 0.000000 | 2.000000 | 2.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 60.869565 | 36.842105 | 23.529412 | 7.692308 | 25.000000 | 12.500000 | 0.000000 | 15.000000 | 0.000000 | 80.000000 | 0.000000 | 0.000000 | 0.000000 | 8.333333 | 0.000000 | 0.000000 | 2.611111 |
| 75% | 53.000000 | 20.000000 | 13.000000 | 7.000000 | 0.000000 | 0.000000 | 5.000000 | 2.000000 | 6.000000 | 3.000000 | 1.000000 | 4.000000 | 4.000000 | 2.000000 | 2.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 0.000000 | 1.000000 | 70.000000 | 45.454545 | 33.333333 | 17.647059 | 37.500000 | 20.000000 | 9.090909 | 25.000000 | 55.555556 | 100.000000 | 20.000000 | 0.000000 | 16.666667 | 50.000000 | 0.000000 | 50.000000 | 2.923077 |
| max | 111.000000 | 41.000000 | 23.000000 | 17.000000 | 2.000000 | 1.000000 | 14.000000 | 16.000000 | 12.000000 | 8.000000 | 5.000000 | 10.000000 | 16.000000 | 15.000000 | 6.000000 | 1.000000 | 1.000000 | 7.000000 | 5.000000 | 8.000000 | 6.000000 | 3.000000 | 5.000000 | 100.000000 | 100.000000 | 75.000000 | 50.000000 | 68.750000 | 80.000000 | 42.857143 | 80.000000 | 100.000000 | 100.000000 | 100.000000 | 75.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 3.833333 |
# Calculate the mean for each of the specified columns
means2 = r_challengers[metrics].mean()
# Rank the means from highest to lowest and plot again
sorted_means2 = means2.sort_values(ascending=False)
# Display max rows
pd.set_option('display.max_rows', None)
print(sorted_means2)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)
Title_Loss_% 69.047172 Win_% 61.308590 Total_Rounds 39.657277 Loss_% 37.258460 Title_Win_% 28.978448 Title_KO_Loss_% 28.471005 Title_Dec_Loss_% 27.892083 Dec_Win_% 26.484512 KO_Win_% 24.026060 Dec_Loss_% 17.045424 Total_Fights 15.150235 KO_Loss_% 14.517436 Title_Sub_Loss_% 12.566713 Title_KO_Win_% 12.364603 Title_Dec_Win_% 11.214045 Sub_Win_% 10.679770 Wins 9.469484 Sub_Loss_% 5.639074 Losses 5.460094 Title_Sub_Win_% 5.305903 Win_by_Decision 4.131455 Win_by_KO_TKO 3.638498 Total_Fights_with_Title 3.089202 Loss_by_Decision 2.600939 Rounds_per_Fight 2.583171 Loss_by_KO_TKO 2.037559 Win_by_Submission 1.676056 Wins_with_Title 1.521127 Losses_with_Title 1.502347 Loss_by_Submission 0.812207 Win_by_KO_TKO_with_Title 0.657277 Loss_by_KO_TKO_with_Title 0.643192 Loss_by_Decision_with_Title 0.591549 Win_by_Decision_with_Title 0.591549 Win_by_Submission_with_Title 0.267606 Loss_by_Submission_with_Title 0.262911 Draws 0.122066 No_Contests 0.098592 Draws_with_Title 0.056338 No_Contests_with_Title 0.009390 dtype: float64
# Champions
r_non_champions = r[r['EITHER_CHAMP'] != 'Yes']
r_non_champions
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | Total_Rounds | Total_Fights | Wins | Losses | Draws | No_Contests | Win_by_KO_TKO | Win_by_Submission | Win_by_Decision | Loss_by_KO_TKO | Loss_by_Submission | Loss_by_Decision | Total_Fights_with_Title | Wins_with_Title | Losses_with_Title | Draws_with_Title | No_Contests_with_Title | Win_by_KO_TKO_with_Title | Win_by_Submission_with_Title | Win_by_Decision_with_Title | Loss_by_KO_TKO_with_Title | Loss_by_Submission_with_Title | Loss_by_Decision_with_Title | Win_% | Loss_% | KO_Win_% | Sub_Win_% | Dec_Win_% | KO_Loss_% | Sub_Loss_% | Dec_Loss_% | Title_Win_% | Title_Loss_% | Title_KO_Win_% | Title_Sub_Win_% | Title_Dec_Win_% | Title_KO_Loss_% | Title_Sub_Loss_% | Title_Dec_Loss_% | Rounds_per_Fight | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5 | Dustin Poirier | Southpaw | No | Yes | Yes | Yes | 71 | 30 | 22 | 7 | 0 | 1 | 11 | 4 | 7 | 3 | 3 | 1 | 3.0 | 1.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 2.0 | 0.0 | 73.333333 | 23.333333 | 36.666667 | 13.333333 | 23.333333 | 10.000000 | 10.000000 | 3.333333 | 33.333333 | 66.666667 | 0.000000 | 0.000000 | 33.333333 | 0.000000 | 66.666667 | 0.000000 | 2.366667 |
| 17 | Rafael Dos Anjos | Southpaw | Yes | No | Yes | Yes | 111 | 35 | 21 | 14 | 0 | 0 | 4 | 5 | 12 | 3 | 1 | 10 | 4.0 | 2.0 | 2.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 | 1.0 | 0.0 | 1.0 | 60.000000 | 40.000000 | 11.428571 | 14.285714 | 34.285714 | 8.571429 | 2.857143 | 28.571429 | 50.000000 | 50.000000 | 25.000000 | 0.000000 | 25.000000 | 25.000000 | 0.000000 | 25.000000 | 3.171429 |
| 22 | Petr Yan | Switch | Yes | Yes | Yes | Yes | 45 | 13 | 9 | 4 | 0 | 0 | 4 | 0 | 5 | 0 | 0 | 3 | 4.0 | 2.0 | 2.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 1.0 | 69.230769 | 30.769231 | 30.769231 | 0.000000 | 38.461538 | 0.000000 | 0.000000 | 23.076923 | 50.000000 | 50.000000 | 25.000000 | 0.000000 | 25.000000 | 0.000000 | 0.000000 | 25.000000 | 3.461538 |
| 27 | Sean O'Malley | Switch | Yes | No | Yes | Yes | 28 | 12 | 10 | 1 | 0 | 1 | 6 | 0 | 4 | 1 | 0 | 0 | 2.0 | 2.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 83.333333 | 8.333333 | 50.000000 | 0.000000 | 33.333333 | 8.333333 | 0.000000 | 0.000000 | 100.000000 | 0.000000 | 50.000000 | 0.000000 | 50.000000 | 0.000000 | 0.000000 | 0.000000 | 2.333333 |
| 50 | Brandon Moreno | Orthodox | Yes | Yes | Yes | Yes | 53 | 16 | 9 | 5 | 2 | 0 | 3 | 3 | 3 | 0 | 0 | 5 | 6.0 | 3.0 | 2.0 | 1.0 | 0.0 | 2.0 | 1.0 | 0.0 | 0.0 | 0.0 | 2.0 | 56.250000 | 31.250000 | 18.750000 | 18.750000 | 18.750000 | 0.000000 | 0.000000 | 31.250000 | 50.000000 | 33.333333 | 33.333333 | 16.666667 | 0.000000 | 0.000000 | 0.000000 | 33.333333 | 3.312500 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2165 | Dave Menne | Orthodox | Yes | No | Yes | Yes | 12 | 5 | 1 | 4 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 1 | 2.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 20.000000 | 80.000000 | 0.000000 | 0.000000 | 20.000000 | 60.000000 | 0.000000 | 20.000000 | 50.000000 | 50.000000 | 0.000000 | 0.000000 | 50.000000 | 50.000000 | 0.000000 | 0.000000 | 2.400000 |
| 2241 | Carlos Newton | Orthodox | Yes | No | Yes | Yes | 13 | 5 | 2 | 3 | 0 | 0 | 0 | 2 | 0 | 2 | 0 | 1 | 3.0 | 1.0 | 2.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 2.0 | 0.0 | 0.0 | 40.000000 | 60.000000 | 0.000000 | 40.000000 | 0.000000 | 40.000000 | 0.000000 | 20.000000 | 33.333333 | 66.666667 | 0.000000 | 33.333333 | 0.000000 | 66.666667 | 0.000000 | 0.000000 | 2.600000 |
| 2246 | Ricco Rodriguez | Orthodox | Yes | No | Yes | Yes | 19 | 7 | 5 | 2 | 0 | 0 | 5 | 0 | 0 | 1 | 0 | 1 | 2.0 | 1.0 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 71.428571 | 28.571429 | 71.428571 | 0.000000 | 0.000000 | 14.285714 | 0.000000 | 14.285714 | 50.000000 | 50.000000 | 50.000000 | 0.000000 | 0.000000 | 50.000000 | 0.000000 | 0.000000 | 2.714286 |
| 2278 | Pat Miletich | Orthodox | Yes | No | Yes | Yes | 10 | 5 | 3 | 2 | 0 | 0 | 1 | 2 | 0 | 1 | 1 | 0 | 3.0 | 2.0 | 1.0 | 0.0 | 0.0 | 0.0 | 2.0 | 0.0 | 0.0 | 1.0 | 0.0 | 60.000000 | 40.000000 | 20.000000 | 40.000000 | 0.000000 | 20.000000 | 20.000000 | 0.000000 | 66.666667 | 33.333333 | 0.000000 | 66.666667 | 0.000000 | 0.000000 | 33.333333 | 0.000000 | 2.000000 |
| 2282 | Kevin Randleman | Orthodox | Yes | No | Yes | Yes | 12 | 4 | 2 | 2 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 2.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 50.000000 | 50.000000 | 0.000000 | 0.000000 | 50.000000 | 50.000000 | 0.000000 | 0.000000 | 50.000000 | 50.000000 | 0.000000 | 0.000000 | 50.000000 | 50.000000 | 0.000000 | 0.000000 | 3.000000 |
103 rows × 46 columns
r_non_champions.describe()
| Total_Rounds | Total_Fights | Wins | Losses | Draws | No_Contests | Win_by_KO_TKO | Win_by_Submission | Win_by_Decision | Loss_by_KO_TKO | Loss_by_Submission | Loss_by_Decision | Total_Fights_with_Title | Wins_with_Title | Losses_with_Title | Draws_with_Title | No_Contests_with_Title | Win_by_KO_TKO_with_Title | Win_by_Submission_with_Title | Win_by_Decision_with_Title | Loss_by_KO_TKO_with_Title | Loss_by_Submission_with_Title | Loss_by_Decision_with_Title | Win_% | Loss_% | KO_Win_% | Sub_Win_% | Dec_Win_% | KO_Loss_% | Sub_Loss_% | Dec_Loss_% | Title_Win_% | Title_Loss_% | Title_KO_Win_% | Title_Sub_Win_% | Title_Dec_Win_% | Title_KO_Loss_% | Title_Sub_Loss_% | Title_Dec_Loss_% | Rounds_per_Fight | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.00000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 | 103.000000 |
| mean | 44.213592 | 16.281553 | 11.048544 | 4.970874 | 0.135922 | 0.126214 | 4.388350 | 1.893204 | 4.747573 | 2.126214 | 0.699029 | 2.126214 | 5.009709 | 3.145631 | 1.766990 | 0.07767 | 0.019417 | 1.359223 | 0.553398 | 1.223301 | 0.844660 | 0.291262 | 0.621359 | 68.529475 | 29.899001 | 28.092761 | 11.631376 | 28.718281 | 13.559472 | 4.239454 | 11.983180 | 59.926305 | 38.417939 | 25.569518 | 10.972401 | 23.190211 | 18.990201 | 6.731812 | 12.453208 | 2.694256 |
| std | 20.954503 | 7.100701 | 4.765848 | 3.270355 | 0.397259 | 0.333714 | 2.719692 | 2.300523 | 3.139622 | 1.877000 | 0.998381 | 2.003329 | 3.462673 | 2.894917 | 1.198069 | 0.26896 | 0.138662 | 1.454232 | 0.987452 | 1.644552 | 0.967687 | 0.571051 | 0.930070 | 13.587318 | 13.441360 | 16.901302 | 11.849880 | 16.191080 | 11.346138 | 5.935712 | 9.536207 | 21.417125 | 21.865765 | 24.033066 | 18.117930 | 25.491374 | 20.576808 | 14.258026 | 18.071435 | 0.575128 |
| min | 9.000000 | 4.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 1.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 20.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 20.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.125000 |
| 25% | 28.000000 | 12.000000 | 8.000000 | 2.000000 | 0.000000 | 0.000000 | 2.000000 | 0.000000 | 2.000000 | 1.000000 | 0.000000 | 1.000000 | 3.000000 | 1.000000 | 1.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 60.000000 | 21.428571 | 16.025641 | 0.000000 | 18.465909 | 6.250000 | 0.000000 | 4.673913 | 50.000000 | 25.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 2.345238 |
| 50% | 44.000000 | 15.000000 | 11.000000 | 4.000000 | 0.000000 | 0.000000 | 4.000000 | 1.000000 | 4.000000 | 2.000000 | 0.000000 | 2.000000 | 4.000000 | 2.000000 | 2.000000 | 0.00000 | 0.000000 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 0.000000 | 0.000000 | 69.230769 | 30.000000 | 25.000000 | 9.523810 | 28.571429 | 12.500000 | 0.000000 | 11.111111 | 57.142857 | 40.000000 | 25.000000 | 0.000000 | 20.000000 | 15.384615 | 0.000000 | 0.000000 | 2.666667 |
| 75% | 58.000000 | 20.000000 | 14.000000 | 7.000000 | 0.000000 | 0.000000 | 6.000000 | 3.000000 | 7.000000 | 3.000000 | 1.000000 | 3.000000 | 6.000000 | 4.000000 | 2.000000 | 0.00000 | 0.000000 | 2.000000 | 1.000000 | 1.500000 | 1.000000 | 0.000000 | 1.000000 | 75.595238 | 37.797619 | 37.980769 | 19.375000 | 39.230769 | 18.347339 | 7.142857 | 19.523810 | 75.000000 | 50.000000 | 38.750000 | 18.333333 | 36.931818 | 33.333333 | 0.000000 | 20.000000 | 3.000000 |
| max | 111.000000 | 41.000000 | 23.000000 | 17.000000 | 2.000000 | 1.000000 | 11.000000 | 16.000000 | 12.000000 | 8.000000 | 4.000000 | 10.000000 | 16.000000 | 15.000000 | 6.000000 | 1.00000 | 1.000000 | 7.000000 | 5.000000 | 8.000000 | 6.000000 | 3.000000 | 5.000000 | 100.000000 | 80.000000 | 71.428571 | 50.000000 | 64.285714 | 60.000000 | 23.076923 | 35.714286 | 100.000000 | 80.000000 | 100.000000 | 75.000000 | 100.000000 | 66.666667 | 66.666667 | 66.666667 | 3.833333 |
# Calculate the mean for each of the specified columns
means3 = r_non_champions[metrics].mean()
# Rank the means from highest to lowest and plot again
sorted_means3 = means3.sort_values(ascending=False)
# Display max rows
pd.set_option('display.max_rows', None)
print(sorted_means3)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)
Win_% 68.529475 Title_Win_% 59.926305 Total_Rounds 44.213592 Title_Loss_% 38.417939 Loss_% 29.899001 Dec_Win_% 28.718281 KO_Win_% 28.092761 Title_KO_Win_% 25.569518 Title_Dec_Win_% 23.190211 Title_KO_Loss_% 18.990201 Total_Fights 16.281553 KO_Loss_% 13.559472 Title_Dec_Loss_% 12.453208 Dec_Loss_% 11.983180 Sub_Win_% 11.631376 Wins 11.048544 Title_Sub_Win_% 10.972401 Title_Sub_Loss_% 6.731812 Total_Fights_with_Title 5.009709 Losses 4.970874 Win_by_Decision 4.747573 Win_by_KO_TKO 4.388350 Sub_Loss_% 4.239454 Wins_with_Title 3.145631 Rounds_per_Fight 2.694256 Loss_by_Decision 2.126214 Loss_by_KO_TKO 2.126214 Win_by_Submission 1.893204 Losses_with_Title 1.766990 Win_by_KO_TKO_with_Title 1.359223 Win_by_Decision_with_Title 1.223301 Loss_by_KO_TKO_with_Title 0.844660 Loss_by_Submission 0.699029 Loss_by_Decision_with_Title 0.621359 Win_by_Submission_with_Title 0.553398 Loss_by_Submission_with_Title 0.291262 Draws 0.135922 No_Contests 0.126214 Draws_with_Title 0.077670 No_Contests_with_Title 0.019417 dtype: float64
# Challengers
r_non_challengers = r[r['EVER_CHALLENGER'] != 'Yes']
r_non_challengers
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | Total_Rounds | Total_Fights | Wins | Losses | Draws | No_Contests | Win_by_KO_TKO | Win_by_Submission | Win_by_Decision | Loss_by_KO_TKO | Loss_by_Submission | Loss_by_Decision | Total_Fights_with_Title | Wins_with_Title | Losses_with_Title | Draws_with_Title | No_Contests_with_Title | Win_by_KO_TKO_with_Title | Win_by_Submission_with_Title | Win_by_Decision_with_Title | Loss_by_KO_TKO_with_Title | Loss_by_Submission_with_Title | Loss_by_Decision_with_Title | Win_% | Loss_% | KO_Win_% | Sub_Win_% | Dec_Win_% | KO_Loss_% | Sub_Loss_% | Dec_Loss_% | Title_Win_% | Title_Loss_% | Title_KO_Win_% | Title_Sub_Win_% | Title_Dec_Win_% | Title_KO_Loss_% | Title_Sub_Loss_% | Title_Dec_Loss_% | Rounds_per_Fight | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5 | Dustin Poirier | Southpaw | No | Yes | Yes | Yes | 71 | 30 | 22 | 7 | 0 | 1 | 11 | 4 | 7 | 3 | 3 | 1 | 3.0 | 1.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 2.0 | 0.0 | 73.333333 | 23.333333 | 36.666667 | 13.333333 | 23.333333 | 10.000000 | 10.000000 | 3.333333 | 33.333333 | 66.666667 | 0.0 | 0.000000 | 33.333333 | 0.000000 | 66.666667 | 0.000000 | 2.366667 |
| 6 | Gilbert Burns | Orthodox | No | No | Yes | No | 57 | 22 | 15 | 7 | 0 | 0 | 3 | 5 | 7 | 3 | 0 | 4 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 68.181818 | 31.818182 | 13.636364 | 22.727273 | 31.818182 | 13.636364 | 0.000000 | 18.181818 | 0.000000 | 100.000000 | 0.0 | 0.000000 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 2.590909 |
| 17 | Rafael Dos Anjos | Southpaw | Yes | No | Yes | Yes | 111 | 35 | 21 | 14 | 0 | 0 | 4 | 5 | 12 | 3 | 1 | 10 | 4.0 | 2.0 | 2.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 | 1.0 | 0.0 | 1.0 | 60.000000 | 40.000000 | 11.428571 | 14.285714 | 34.285714 | 8.571429 | 2.857143 | 28.571429 | 50.000000 | 50.000000 | 25.0 | 0.000000 | 25.000000 | 25.000000 | 0.000000 | 25.000000 | 3.171429 |
| 22 | Petr Yan | Switch | Yes | Yes | Yes | Yes | 45 | 13 | 9 | 4 | 0 | 0 | 4 | 0 | 5 | 0 | 0 | 3 | 4.0 | 2.0 | 2.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 1.0 | 69.230769 | 30.769231 | 30.769231 | 0.000000 | 38.461538 | 0.000000 | 0.000000 | 23.076923 | 50.000000 | 50.000000 | 25.0 | 0.000000 | 25.000000 | 0.000000 | 0.000000 | 25.000000 | 3.461538 |
| 26 | Marlon Vera | Switch | No | No | Yes | No | 66 | 23 | 15 | 8 | 0 | 0 | 7 | 4 | 4 | 0 | 0 | 8 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 65.217391 | 34.782609 | 30.434783 | 17.391304 | 17.391304 | 0.000000 | 0.000000 | 34.782609 | 0.000000 | 100.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | 2.869565 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2245 | Pedro Rizzo | Orthodox | No | No | Yes | No | 28 | 10 | 5 | 5 | 0 | 0 | 4 | 0 | 1 | 2 | 0 | 3 | 3.0 | 0.0 | 3.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 2.0 | 50.000000 | 50.000000 | 40.000000 | 0.000000 | 10.000000 | 20.000000 | 0.000000 | 30.000000 | 0.000000 | 100.000000 | 0.0 | 0.000000 | 0.000000 | 33.333333 | 0.000000 | 66.666667 | 2.800000 |
| 2246 | Ricco Rodriguez | Orthodox | Yes | No | Yes | Yes | 19 | 7 | 5 | 2 | 0 | 0 | 5 | 0 | 0 | 1 | 0 | 1 | 2.0 | 1.0 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 71.428571 | 28.571429 | 71.428571 | 0.000000 | 0.000000 | 14.285714 | 0.000000 | 14.285714 | 50.000000 | 50.000000 | 50.0 | 0.000000 | 0.000000 | 50.000000 | 0.000000 | 0.000000 | 2.714286 |
| 2251 | Gan McGee | Orthodox | No | No | Yes | No | 5 | 4 | 2 | 2 | 0 | 0 | 2 | 0 | 0 | 2 | 0 | 0 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 50.000000 | 50.000000 | 50.000000 | 0.000000 | 0.000000 | 50.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | 0.0 | 0.000000 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 1.250000 |
| 2278 | Pat Miletich | Orthodox | Yes | No | Yes | Yes | 10 | 5 | 3 | 2 | 0 | 0 | 1 | 2 | 0 | 1 | 1 | 0 | 3.0 | 2.0 | 1.0 | 0.0 | 0.0 | 0.0 | 2.0 | 0.0 | 0.0 | 1.0 | 0.0 | 60.000000 | 40.000000 | 20.000000 | 40.000000 | 0.000000 | 20.000000 | 20.000000 | 0.000000 | 66.666667 | 33.333333 | 0.0 | 66.666667 | 0.000000 | 0.000000 | 33.333333 | 0.000000 | 2.000000 |
| 2282 | Kevin Randleman | Orthodox | Yes | No | Yes | Yes | 12 | 4 | 2 | 2 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 2.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 50.000000 | 50.000000 | 0.000000 | 0.000000 | 50.000000 | 50.000000 | 0.000000 | 0.000000 | 50.000000 | 50.000000 | 0.0 | 0.000000 | 50.000000 | 50.000000 | 0.000000 | 0.000000 | 3.000000 |
213 rows × 46 columns
r_non_challengers.describe()
| Total_Rounds | Total_Fights | Wins | Losses | Draws | No_Contests | Win_by_KO_TKO | Win_by_Submission | Win_by_Decision | Loss_by_KO_TKO | Loss_by_Submission | Loss_by_Decision | Total_Fights_with_Title | Wins_with_Title | Losses_with_Title | Draws_with_Title | No_Contests_with_Title | Win_by_KO_TKO_with_Title | Win_by_Submission_with_Title | Win_by_Decision_with_Title | Loss_by_KO_TKO_with_Title | Loss_by_Submission_with_Title | Loss_by_Decision_with_Title | Win_% | Loss_% | KO_Win_% | Sub_Win_% | Dec_Win_% | KO_Loss_% | Sub_Loss_% | Dec_Loss_% | Title_Win_% | Title_Loss_% | Title_KO_Win_% | Title_Sub_Win_% | Title_Dec_Win_% | Title_KO_Loss_% | Title_Sub_Loss_% | Title_Dec_Loss_% | Rounds_per_Fight | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 | 213.000000 |
| mean | 39.657277 | 15.150235 | 9.469484 | 5.460094 | 0.122066 | 0.098592 | 3.638498 | 1.676056 | 4.131455 | 2.037559 | 0.812207 | 2.600939 | 3.089202 | 1.521127 | 1.502347 | 0.056338 | 0.009390 | 0.657277 | 0.267606 | 0.591549 | 0.643192 | 0.262911 | 0.591549 | 61.308590 | 37.258460 | 24.026060 | 10.679770 | 26.484512 | 14.517436 | 5.639074 | 17.045424 | 28.978448 | 69.047172 | 12.364603 | 5.305903 | 11.214045 | 28.471005 | 12.566713 | 27.892083 | 2.583171 |
| std | 19.794460 | 6.967984 | 4.881159 | 3.050604 | 0.368745 | 0.298815 | 2.836059 | 2.174735 | 2.955902 | 1.845059 | 1.108476 | 2.247724 | 3.072478 | 2.552427 | 0.979143 | 0.231116 | 0.096672 | 1.216981 | 0.738901 | 1.294883 | 0.803538 | 0.519783 | 0.799365 | 15.596605 | 15.593465 | 16.740287 | 11.575076 | 16.264009 | 13.644809 | 7.905501 | 14.018623 | 33.492435 | 34.532522 | 21.022367 | 13.716553 | 21.156069 | 37.177658 | 28.624965 | 38.650714 | 0.546618 |
| min | 4.000000 | 4.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 |
| 25% | 23.000000 | 10.000000 | 6.000000 | 3.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 2.000000 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 53.846154 | 28.571429 | 12.500000 | 0.000000 | 14.285714 | 5.882353 | 0.000000 | 7.142857 | 0.000000 | 40.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 2.250000 |
| 50% | 38.000000 | 14.000000 | 9.000000 | 5.000000 | 0.000000 | 0.000000 | 3.000000 | 1.000000 | 4.000000 | 2.000000 | 0.000000 | 2.000000 | 2.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 60.869565 | 36.842105 | 23.529412 | 7.692308 | 25.000000 | 12.500000 | 0.000000 | 15.000000 | 0.000000 | 80.000000 | 0.000000 | 0.000000 | 0.000000 | 8.333333 | 0.000000 | 0.000000 | 2.611111 |
| 75% | 53.000000 | 20.000000 | 13.000000 | 7.000000 | 0.000000 | 0.000000 | 5.000000 | 2.000000 | 6.000000 | 3.000000 | 1.000000 | 4.000000 | 4.000000 | 2.000000 | 2.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 0.000000 | 1.000000 | 70.000000 | 45.454545 | 33.333333 | 17.647059 | 37.500000 | 20.000000 | 9.090909 | 25.000000 | 55.555556 | 100.000000 | 20.000000 | 0.000000 | 16.666667 | 50.000000 | 0.000000 | 50.000000 | 2.923077 |
| max | 111.000000 | 41.000000 | 23.000000 | 17.000000 | 2.000000 | 1.000000 | 14.000000 | 16.000000 | 12.000000 | 8.000000 | 5.000000 | 10.000000 | 16.000000 | 15.000000 | 6.000000 | 1.000000 | 1.000000 | 7.000000 | 5.000000 | 8.000000 | 6.000000 | 3.000000 | 5.000000 | 100.000000 | 100.000000 | 75.000000 | 50.000000 | 68.750000 | 80.000000 | 42.857143 | 80.000000 | 100.000000 | 100.000000 | 100.000000 | 75.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 3.833333 |
# Calculate the mean for each of the specified columns
means4 = r_non_challengers[metrics].mean()
# Rank the means from highest to lowest and plot again
sorted_means4 = means4.sort_values(ascending=False)
# Display max rows
pd.set_option('display.max_rows', None)
print(sorted_means4)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)
Title_Loss_% 69.047172 Win_% 61.308590 Total_Rounds 39.657277 Loss_% 37.258460 Title_Win_% 28.978448 Title_KO_Loss_% 28.471005 Title_Dec_Loss_% 27.892083 Dec_Win_% 26.484512 KO_Win_% 24.026060 Dec_Loss_% 17.045424 Total_Fights 15.150235 KO_Loss_% 14.517436 Title_Sub_Loss_% 12.566713 Title_KO_Win_% 12.364603 Title_Dec_Win_% 11.214045 Sub_Win_% 10.679770 Wins 9.469484 Sub_Loss_% 5.639074 Losses 5.460094 Title_Sub_Win_% 5.305903 Win_by_Decision 4.131455 Win_by_KO_TKO 3.638498 Total_Fights_with_Title 3.089202 Loss_by_Decision 2.600939 Rounds_per_Fight 2.583171 Loss_by_KO_TKO 2.037559 Win_by_Submission 1.676056 Wins_with_Title 1.521127 Losses_with_Title 1.502347 Loss_by_Submission 0.812207 Win_by_KO_TKO_with_Title 0.657277 Loss_by_KO_TKO_with_Title 0.643192 Loss_by_Decision_with_Title 0.591549 Win_by_Decision_with_Title 0.591549 Win_by_Submission_with_Title 0.267606 Loss_by_Submission_with_Title 0.262911 Draws 0.122066 No_Contests 0.098592 Draws_with_Title 0.056338 No_Contests_with_Title 0.009390 dtype: float64
# Display max rows
pd.set_option('display.max_rows', None)
breakdown(event_buyrate)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)
Distinct Count EVENT 209 DATE 209 Buyrate 108 dtype: int64 Count EVENT 209 DATE 209 Buyrate 209 dtype: int64 DTypes EVENT object DATE object Buyrate float64 dtype: object Shape (209, 3) Nulls EVENT 0 DATE 0 Buyrate 0 dtype: int64 Null % EVENT 0.0 DATE 0.0 Buyrate 0.0 dtype: float64
# Close the cursor and connection
cursor.close()
conn.close()
fb = fighter_buyrate[fighter_buyrate['PPV_Fights'] > 1]
fb.describe()
| Buyrate | Avg_Buyrate_Per_Event | PPV_Fights | Buyrate_PERCENTILE | PPV_Fights_PERCENTILE | |
|---|---|---|---|---|---|
| count | 8.230000e+02 | 8.230000e+02 | 823.000000 | 823.000000 | 823.000000 |
| mean | 2.313442e+06 | 4.825095e+05 | 4.780073 | 79.756446 | 70.260492 |
| std | 1.993544e+06 | 2.322253e+05 | 3.604403 | 13.905212 | 16.825467 |
| min | 7.500000e+04 | 3.750000e+04 | 2.000000 | 41.324989 | 49.963821 |
| 25% | 9.450000e+05 | 3.333333e+05 | 2.000000 | 70.576176 | 49.963821 |
| 50% | 1.685000e+06 | 4.625000e+05 | 3.000000 | 81.916271 | 65.484805 |
| 75% | 3.077500e+06 | 5.944286e+05 | 6.000000 | 91.119983 | 85.419682 |
| max | 1.355517e+07 | 1.630000e+06 | 22.000000 | 100.000000 | 99.927641 |
# Histogram
fb.hist(figsize=(10, 8))
# Box Plot
plt.figure(figsize=(10, 7))
sns.boxplot(data=fb, orient="v")
# Pair Plot
sns.pairplot(fb, hue="STANCE")
<seaborn.axisgrid.PairGrid at 0x7fe0fd305af0>
fb.corr()
| Buyrate | Avg_Buyrate_Per_Event | PPV_Fights | Buyrate_PERCENTILE | PPV_Fights_PERCENTILE | |
|---|---|---|---|---|---|
| Buyrate | 1.000000 | 0.412245 | 0.867996 | 0.803415 | 0.758139 |
| Avg_Buyrate_Per_Event | 0.412245 | 1.000000 | 0.008387 | 0.612128 | 0.016307 |
| PPV_Fights | 0.867996 | 0.008387 | 1.000000 | 0.662269 | 0.860752 |
| Buyrate_PERCENTILE | 0.803415 | 0.612128 | 0.662269 | 1.000000 | 0.745089 |
| PPV_Fights_PERCENTILE | 0.758139 | 0.016307 | 0.860752 | 0.745089 | 1.000000 |
correlation_matrix = fb[['Buyrate', 'Avg_Buyrate_Per_Event', 'PPV_Fights']].corr()
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Heatmap')
plt.show()
# Plotting box plots for the numerical columns
plt.figure(figsize=(12, 6))
# Subplot 1: Buyrate
plt.subplot(1, 3, 1)
sns.boxplot(y=fb['Buyrate'])
plt.title('Box Plot of Buyrate')
# Subplot 2: Avg_Buyrate_Per_Event
plt.subplot(1, 3, 2)
sns.boxplot(y=fb['Avg_Buyrate_Per_Event'].dropna()) # Drop NA for visualization
plt.title('Box Plot of Avg Buyrate Per Event')
# Subplot 3: PPV_Fights
plt.subplot(1, 3, 3)
sns.boxplot(y=fb['PPV_Fights'].dropna()) # Drop NA for visualization
plt.title('Box Plot of PPV Fights')
plt.tight_layout()
plt.show()
plt.figure(figsize=(10, 6))
sns.countplot(x='STANCE', data=fb, palette='Set2')
plt.title('Population Distribution by Stance')
plt.xlabel('Stance')
plt.ylabel('Count')
plt.show()
# For demonstration, we'll filter the dataset for 'Orthodox' and 'Southpaw' stances only
# and consider the 'Buyrate' column for A/B testing
orthodox_buyrates = fb[fb['STANCE'] == 'Orthodox']['Buyrate']
southpaw_buyrates = fb[fb['STANCE'] == 'Southpaw']['Buyrate']
# Perform a t-test between the two groups
t_stat, p_value = ttest_ind(orthodox_buyrates, southpaw_buyrates, nan_policy='omit') # 'omit' to ignore NaNs
t_stat, p_value
(-2.376810039412822, 0.017700976328208187)
champion_buyrates = fb[(fb['EVER_CHAMPION'] == 'Yes') | (fb['EVER_INTERIM'] == 'Yes')]['Buyrate']
non_champion_buyrates = fb[(fb['EVER_CHAMPION'] != 'Yes') | (fb['EVER_INTERIM'] != 'Yes')]['Buyrate']
# Perform a t-test between the two groups
t_stat, p_value = ttest_ind(champion_buyrates, non_champion_buyrates)
t_stat, p_value
(10.324081823558389, 1.0758014507380182e-23)
# Perform ANOVA test
f_stat, p_value = f_oneway(champion_buyrates, non_champion_buyrates)
f_stat, p_value
(106.58666549952886, 1.0758014507374742e-23)
# Perform chi-square test
chi2, p_value, dof, expected = chi2_contingency(fb[['Buyrate', 'Avg_Buyrate_Per_Event', 'PPV_Fights']])
print(f"Chi-square statistic: {chi2}, P-value: {p_value}")
Chi-square statistic: 138812015.36374083, P-value: 0.0
# Number of successes (conversions) and samples in each group
successes = np.array([300, 350])
samples = np.array([1000, 1000])
# Perform z-test
z_stat, p_value = proportions_ztest(count=successes, nobs=samples)
print(f"Z-statistic: {z_stat}, P-value: {p_value}")
Z-statistic: -2.3870495801314426, P-value: 0.01698420058213061
# Counting the values in the STANCE column
stance_count = records["STANCE"].value_counts()
# Calculating the percentage of each stance
stance_percentage = records["STANCE"].value_counts(normalize=True) * 100
# Combine count and percentage into a single DataFrame for a better display
stance_summary = pd.DataFrame({'Count': stance_count, 'Percentage': stance_percentage})
stance_summary
| Count | Percentage | |
|---|---|---|
| Orthodox | 1601 | 77.156627 |
| Southpaw | 360 | 17.349398 |
| Switch | 114 | 5.493976 |
# Plotting the counts of the STANCE values
fig, ax = plt.subplots(1, 2, figsize=(14, 6))
# Count plot
stance_count.plot(kind='bar', ax=ax[0], color='sandybrown')
ax[0].set_title('Count of Fighters by Stance')
ax[0].set_xlabel('Stance')
ax[0].set_ylabel('Count')
ax[0].set_xticklabels(ax[0].get_xticklabels(), rotation=45, ha='right')
# Percentage plot
stance_percentage.plot(kind='pie', ax=ax[1], autopct='%1.1f%%', startangle=140, colors=['skyblue', 'lightgreen', 'salmon', 'gold'])
ax[1].set_ylabel('')
ax[1].set_title('Percentage of Fighters by Stance')
plt.tight_layout()
plt.show()
# Looking at just champions
champion_records = records[(records['EVER_CHAMPION'] == 'Yes') | (records['EVER_INTERIM'] == 'Yes')]
champion_records.sort_values(by='Wins_with_Title', ascending=False, na_position='last')
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | Total_Rounds | Total_Fights | Wins | Losses | Draws | No_Contests | Win_by_KO_TKO | Win_by_Submission | Win_by_Decision | Loss_by_KO_TKO | Loss_by_Submission | Loss_by_Decision | Total_Fights_with_Title | Wins_with_Title | Losses_with_Title | Draws_with_Title | No_Contests_with_Title | Win_by_KO_TKO_with_Title | Win_by_Submission_with_Title | Win_by_Decision_with_Title | Loss_by_KO_TKO_with_Title | Loss_by_Submission_with_Title | Loss_by_Decision_with_Title | Win_% | Loss_% | KO_Win_% | Sub_Win_% | Dec_Win_% | KO_Loss_% | Sub_Loss_% | Dec_Loss_% | Title_Win_% | Title_Loss_% | Title_KO_Win_% | Title_Sub_Win_% | Title_Dec_Win_% | Title_KO_Loss_% | Title_Sub_Loss_% | Title_Dec_Loss_% | Rounds_per_Fight | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 617 | Jon Jones | Orthodox | Yes | Yes | Yes | Yes | 74 | 23 | 21 | 1 | 0 | 1 | 5 | 6 | 10 | 0 | 0 | 0 | 16.0 | 15.0 | 0.0 | 0.0 | 1.0 | 3.0 | 4.0 | 8.0 | 0.0 | 0.0 | 0.0 | 91.304348 | 4.347826 | 21.739130 | 26.086957 | 43.478261 | 0.000000 | 0.000000 | 0.000000 | 93.750000 | 0.000000 | 18.750000 | 25.000000 | 50.000000 | 0.000000 | 0.000000 | 0.000000 | 3.217391 |
| 1262 | Georges St-Pierre | Orthodox | Yes | Yes | Yes | Yes | 71 | 22 | 20 | 2 | 0 | 0 | 5 | 3 | 12 | 1 | 1 | 0 | 15.0 | 13.0 | 2.0 | 0.0 | 0.0 | 3.0 | 2.0 | 8.0 | 1.0 | 1.0 | 0.0 | 90.909091 | 9.090909 | 22.727273 | 13.636364 | 54.545455 | 4.545455 | 4.545455 | 0.000000 | 86.666667 | 13.333333 | 20.000000 | 13.333333 | 53.333333 | 6.666667 | 6.666667 | 0.000000 | 3.227273 |
| 1174 | Demetrious Johnson | Orthodox | Yes | No | Yes | Yes | 69 | 18 | 15 | 2 | 1 | 0 | 2 | 5 | 8 | 0 | 0 | 2 | 14.0 | 12.0 | 2.0 | 0.0 | 0.0 | 2.0 | 5.0 | 5.0 | 0.0 | 0.0 | 2.0 | 83.333333 | 11.111111 | 11.111111 | 27.777778 | 44.444444 | 0.000000 | 0.000000 | 11.111111 | 85.714286 | 14.285714 | 14.285714 | 35.714286 | 35.714286 | 0.000000 | 0.000000 | 14.285714 | 3.833333 |
| 946 | Anderson Silva | Southpaw | Yes | No | Yes | Yes | 63 | 25 | 17 | 7 | 0 | 1 | 11 | 3 | 3 | 4 | 0 | 3 | 13.0 | 11.0 | 2.0 | 0.0 | 0.0 | 7.0 | 2.0 | 2.0 | 2.0 | 0.0 | 0.0 | 68.000000 | 28.000000 | 44.000000 | 12.000000 | 12.000000 | 16.000000 | 0.000000 | 12.000000 | 84.615385 | 15.384615 | 53.846154 | 15.384615 | 15.384615 | 15.384615 | 0.000000 | 0.000000 | 2.520000 |
| 527 | Amanda Nunes | Orthodox | Yes | No | Yes | Yes | 47 | 18 | 16 | 2 | 0 | 0 | 7 | 3 | 6 | 1 | 1 | 0 | 12.0 | 11.0 | 1.0 | 0.0 | 0.0 | 4.0 | 2.0 | 5.0 | 0.0 | 1.0 | 0.0 | 88.888889 | 11.111111 | 38.888889 | 16.666667 | 33.333333 | 5.555556 | 5.555556 | 0.000000 | 91.666667 | 8.333333 | 33.333333 | 16.666667 | 41.666667 | 0.000000 | 8.333333 | 0.000000 | 2.611111 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 873 | Carlos Condit | Orthodox | No | Yes | Yes | Yes | 54 | 19 | 9 | 10 | 0 | 0 | 5 | 0 | 4 | 1 | 3 | 6 | 3.0 | 1.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 2.0 | 47.368421 | 52.631579 | 26.315789 | 0.000000 | 21.052632 | 5.263158 | 15.789474 | 31.578947 | 33.333333 | 66.666667 | 0.000000 | 0.000000 | 33.333333 | 0.000000 | 0.000000 | 66.666667 | 2.842105 |
| 956 | Germaine de Randamie | Orthodox | Yes | No | Yes | Yes | 24 | 9 | 7 | 2 | 0 | 0 | 3 | 1 | 3 | 1 | 0 | 1 | 2.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 1.0 | 77.777778 | 22.222222 | 33.333333 | 11.111111 | 33.333333 | 11.111111 | 0.000000 | 11.111111 | 50.000000 | 50.000000 | 0.000000 | 0.000000 | 50.000000 | 0.000000 | 0.000000 | 50.000000 | 2.666667 |
| 1001 | Yoel Romero | Southpaw | No | Yes | Yes | Yes | 43 | 13 | 9 | 4 | 0 | 0 | 7 | 0 | 2 | 0 | 0 | 4 | 3.0 | 1.0 | 2.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 69.230769 | 30.769231 | 53.846154 | 0.000000 | 15.384615 | 0.000000 | 0.000000 | 30.769231 | 33.333333 | 66.666667 | 33.333333 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 66.666667 | 3.307692 |
| 1073 | Nicco Montano | Southpaw | Yes | No | Yes | Yes | 8 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 50.000000 | 50.000000 | 0.000000 | 0.000000 | 50.000000 | 0.000000 | 0.000000 | 50.000000 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | 4.000000 |
| 2282 | Kevin Randleman | Orthodox | Yes | No | Yes | Yes | 12 | 4 | 2 | 2 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 2.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 50.000000 | 50.000000 | 0.000000 | 0.000000 | 50.000000 | 50.000000 | 0.000000 | 0.000000 | 50.000000 | 50.000000 | 0.000000 | 0.000000 | 50.000000 | 50.000000 | 0.000000 | 0.000000 | 3.000000 |
105 rows × 46 columns
# Champions make up about 4.5% of the fighter population
105/2317
0.045317220543806644
# Counting the values in the STANCE column
champion_stance_count = champion_records["STANCE"].value_counts()
# Calculating the percentage of each stance
champion_stance_percentage = champion_records["STANCE"].value_counts(normalize=True) * 100
# Combine count and percentage into a single DataFrame for a better display
champion_stance_summary = pd.DataFrame({'Count': champion_stance_count, 'Percentage': champion_stance_percentage})
champion_stance_summary
| Count | Percentage | |
|---|---|---|
| Orthodox | 78 | 74.285714 |
| Southpaw | 20 | 19.047619 |
| Switch | 7 | 6.666667 |
# Plotting the counts of the STANCE values
fig, ax = plt.subplots(1, 2, figsize=(14, 6))
# Count plot
champion_stance_count.plot(kind='bar', ax=ax[0], color='orange')
ax[0].set_title('Champion Count of Fighters by Stance')
ax[0].set_xlabel('Stance')
ax[0].set_ylabel('Count')
ax[0].set_xticklabels(ax[0].get_xticklabels(), rotation=45, ha='right')
# Percentage plot
champion_stance_percentage.plot(kind='pie', ax=ax[1], autopct='%1.1f%%', startangle=140, colors=['turquoise', 'seagreen', 'khaki', 'hotpink'])
ax[1].set_ylabel('')
ax[1].set_title('Champion Percentage of Fighters by Stance')
plt.tight_layout()
plt.show()
# Looking at just challengers
challenger_records = records[(records['EVER_CHALLENGER'] == 'Yes')]
challenger_records
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | Total_Rounds | Total_Fights | Wins | Losses | Draws | No_Contests | Win_by_KO_TKO | Win_by_Submission | Win_by_Decision | Loss_by_KO_TKO | Loss_by_Submission | Loss_by_Decision | Total_Fights_with_Title | Wins_with_Title | Losses_with_Title | Draws_with_Title | No_Contests_with_Title | Win_by_KO_TKO_with_Title | Win_by_Submission_with_Title | Win_by_Decision_with_Title | Loss_by_KO_TKO_with_Title | Loss_by_Submission_with_Title | Loss_by_Decision_with_Title | Win_% | Loss_% | KO_Win_% | Sub_Win_% | Dec_Win_% | KO_Loss_% | Sub_Loss_% | Dec_Loss_% | Title_Win_% | Title_Loss_% | Title_KO_Win_% | Title_Sub_Win_% | Title_Dec_Win_% | Title_KO_Loss_% | Title_Sub_Loss_% | Title_Dec_Loss_% | Rounds_per_Fight | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5 | Dustin Poirier | Southpaw | No | Yes | Yes | Yes | 71 | 30 | 22 | 7 | 0 | 1 | 11 | 4 | 7 | 3 | 3 | 1 | 3.0 | 1.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 2.0 | 0.0 | 73.333333 | 23.333333 | 36.666667 | 13.333333 | 23.333333 | 10.000000 | 10.000000 | 3.333333 | 33.333333 | 66.666667 | 0.0 | 0.000000 | 33.333333 | 0.0 | 66.666667 | 0.0 | 2.366667 |
| 6 | Gilbert Burns | Orthodox | No | No | Yes | No | 57 | 22 | 15 | 7 | 0 | 0 | 3 | 5 | 7 | 3 | 0 | 4 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 68.181818 | 31.818182 | 13.636364 | 22.727273 | 31.818182 | 13.636364 | 0.000000 | 18.181818 | 0.000000 | 100.000000 | 0.0 | 0.000000 | 0.000000 | 100.0 | 0.000000 | 0.0 | 2.590909 |
| 17 | Rafael Dos Anjos | Southpaw | Yes | No | Yes | Yes | 111 | 35 | 21 | 14 | 0 | 0 | 4 | 5 | 12 | 3 | 1 | 10 | 4.0 | 2.0 | 2.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 | 1.0 | 0.0 | 1.0 | 60.000000 | 40.000000 | 11.428571 | 14.285714 | 34.285714 | 8.571429 | 2.857143 | 28.571429 | 50.000000 | 50.000000 | 25.0 | 0.000000 | 25.000000 | 25.0 | 0.000000 | 25.0 | 3.171429 |
| 22 | Petr Yan | Switch | Yes | Yes | Yes | Yes | 45 | 13 | 9 | 4 | 0 | 0 | 4 | 0 | 5 | 0 | 0 | 3 | 4.0 | 2.0 | 2.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 1.0 | 69.230769 | 30.769231 | 30.769231 | 0.000000 | 38.461538 | 0.000000 | 0.000000 | 23.076923 | 50.000000 | 50.000000 | 25.0 | 0.000000 | 25.000000 | 0.0 | 0.000000 | 25.0 | 3.461538 |
| 26 | Marlon Vera | Switch | No | No | Yes | No | 66 | 23 | 15 | 8 | 0 | 0 | 7 | 4 | 4 | 0 | 0 | 8 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 65.217391 | 34.782609 | 30.434783 | 17.391304 | 17.391304 | 0.000000 | 0.000000 | 34.782609 | 0.000000 | 100.000000 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 100.0 | 2.869565 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2277 | Hayato Sakurai | Orthodox | No | No | Yes | No | 4 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | 0.0 | 0.000000 | 0.000000 | 100.0 | 0.000000 | 0.0 | 4.000000 |
| 2278 | Pat Miletich | Orthodox | Yes | No | Yes | Yes | 10 | 5 | 3 | 2 | 0 | 0 | 1 | 2 | 0 | 1 | 1 | 0 | 3.0 | 2.0 | 1.0 | 0.0 | 0.0 | 0.0 | 2.0 | 0.0 | 0.0 | 1.0 | 0.0 | 60.000000 | 40.000000 | 20.000000 | 40.000000 | 0.000000 | 20.000000 | 20.000000 | 0.000000 | 66.666667 | 33.333333 | 0.0 | 66.666667 | 0.000000 | 0.0 | 33.333333 | 0.0 | 2.000000 |
| 2282 | Kevin Randleman | Orthodox | Yes | No | Yes | Yes | 12 | 4 | 2 | 2 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 2.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 50.000000 | 50.000000 | 0.000000 | 0.000000 | 50.000000 | 50.000000 | 0.000000 | 0.000000 | 50.000000 | 50.000000 | 0.0 | 0.000000 | 50.000000 | 50.0 | 0.000000 | 0.0 | 3.000000 |
| 2289 | Yuki Kondo | Southpaw | No | No | Yes | No | 7 | 3 | 1 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 33.333333 | 66.666667 | 33.333333 | 0.000000 | 0.000000 | 0.000000 | 33.333333 | 33.333333 | 0.000000 | 100.000000 | 0.0 | 0.000000 | 0.000000 | 0.0 | 100.000000 | 0.0 | 2.333333 |
| 2296 | Kenichi Yamamoto | Orthodox | No | No | Yes | No | 2 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 100.000000 | 0.0 | 0.000000 | 0.000000 | 0.0 | 100.000000 | 0.0 | 2.000000 |
221 rows × 46 columns
# Challengers make up about 9.5% of the fighter population
221/2317
0.0953819594302978
# Counting the values in the STANCE column
challenger_stance_count = challenger_records["STANCE"].value_counts()
# Calculating the percentage of each stance
challenger_stance_percentage = challenger_records["STANCE"].value_counts(normalize=True) * 100
# Combine count and percentage into a single DataFrame for a better display
challenger_stance_summary = pd.DataFrame({'Count': challenger_stance_count, 'Percentage': challenger_stance_percentage})
challenger_stance_summary
| Count | Percentage | |
|---|---|---|
| Orthodox | 163 | 73.755656 |
| Southpaw | 46 | 20.814480 |
| Switch | 12 | 5.429864 |
top_1 = records[records['FIGHTER'].isin(top_1_percent_list)]
top_1
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | Total_Rounds | Total_Fights | Wins | Losses | Draws | No_Contests | Win_by_KO_TKO | Win_by_Submission | Win_by_Decision | Loss_by_KO_TKO | Loss_by_Submission | Loss_by_Decision | Total_Fights_with_Title | Wins_with_Title | Losses_with_Title | Draws_with_Title | No_Contests_with_Title | Win_by_KO_TKO_with_Title | Win_by_Submission_with_Title | Win_by_Decision_with_Title | Loss_by_KO_TKO_with_Title | Loss_by_Submission_with_Title | Loss_by_Decision_with_Title | Win_% | Loss_% | KO_Win_% | Sub_Win_% | Dec_Win_% | KO_Loss_% | Sub_Loss_% | Dec_Loss_% | Title_Win_% | Title_Loss_% | Title_KO_Win_% | Title_Sub_Win_% | Title_Dec_Win_% | Title_KO_Loss_% | Title_Sub_Loss_% | Title_Dec_Loss_% | Rounds_per_Fight | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5 | Dustin Poirier | Southpaw | No | Yes | Yes | Yes | 71 | 30 | 22 | 7 | 0 | 1 | 11 | 4 | 7 | 3 | 3 | 1 | 3.0 | 1.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 2.0 | 0.0 | 73.333333 | 23.333333 | 36.666667 | 13.333333 | 23.333333 | 10.000000 | 10.000000 | 3.333333 | 33.333333 | 66.666667 | 0.000000 | 0.000000 | 33.333333 | 0.000000 | 66.666667 | 0.000000 | 2.366667 |
| 181 | Jim Miller | Southpaw | No | No | No | No | 96 | 43 | 26 | 16 | 0 | 1 | 6 | 12 | 8 | 2 | 3 | 11 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 60.465116 | 37.209302 | 13.953488 | 27.906977 | 18.604651 | 4.651163 | 6.976744 | 25.581395 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.232558 |
| 617 | Jon Jones | Orthodox | Yes | Yes | Yes | Yes | 74 | 23 | 21 | 1 | 0 | 1 | 5 | 6 | 10 | 0 | 0 | 0 | 16.0 | 15.0 | 0.0 | 0.0 | 1.0 | 3.0 | 4.0 | 8.0 | 0.0 | 0.0 | 0.0 | 91.304348 | 4.347826 | 21.739130 | 26.086957 | 43.478261 | 0.000000 | 0.000000 | 0.000000 | 93.750000 | 0.000000 | 18.750000 | 25.000000 | 50.000000 | 0.000000 | 0.000000 | 0.000000 | 3.217391 |
| 748 | Donald Cerrone | Orthodox | No | No | Yes | No | 81 | 38 | 23 | 14 | 0 | 1 | 10 | 6 | 7 | 8 | 1 | 5 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 60.526316 | 36.842105 | 26.315789 | 15.789474 | 18.421053 | 21.052632 | 2.631579 | 13.157895 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 2.131579 |
| 871 | Jeremy Stephens | Orthodox | No | No | No | No | 86 | 34 | 15 | 18 | 0 | 1 | 8 | 0 | 7 | 3 | 3 | 12 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 44.117647 | 52.941176 | 23.529412 | 0.000000 | 20.588235 | 8.823529 | 8.823529 | 35.294118 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.529412 |
| 874 | Conor McGregor | Southpaw | Yes | Yes | Yes | Yes | 28 | 14 | 10 | 4 | 0 | 0 | 8 | 0 | 2 | 2 | 2 | 0 | 4.0 | 3.0 | 1.0 | 0.0 | 0.0 | 3.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 71.428571 | 28.571429 | 57.142857 | 0.000000 | 14.285714 | 14.285714 | 14.285714 | 0.000000 | 75.000000 | 25.000000 | 75.000000 | 0.000000 | 0.000000 | 0.000000 | 25.000000 | 0.000000 | 2.000000 |
| 884 | Demian Maia | Southpaw | No | No | Yes | No | 89 | 33 | 22 | 11 | 0 | 0 | 1 | 11 | 10 | 2 | 0 | 9 | 2.0 | 0.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 66.666667 | 33.333333 | 3.030303 | 33.333333 | 30.303030 | 6.060606 | 0.000000 | 27.272727 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | 2.696970 |
| 946 | Anderson Silva | Southpaw | Yes | No | Yes | Yes | 63 | 25 | 17 | 7 | 0 | 1 | 11 | 3 | 3 | 4 | 0 | 3 | 13.0 | 11.0 | 2.0 | 0.0 | 0.0 | 7.0 | 2.0 | 2.0 | 2.0 | 0.0 | 0.0 | 68.000000 | 28.000000 | 44.000000 | 12.000000 | 12.000000 | 16.000000 | 0.000000 | 12.000000 | 84.615385 | 15.384615 | 53.846154 | 15.384615 | 15.384615 | 15.384615 | 0.000000 | 0.000000 | 2.520000 |
| 958 | Diego Sanchez | Southpaw | No | No | Yes | No | 84 | 32 | 19 | 13 | 0 | 0 | 6 | 0 | 12 | 4 | 0 | 9 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 59.375000 | 40.625000 | 18.750000 | 0.000000 | 37.500000 | 12.500000 | 0.000000 | 28.125000 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 2.625000 |
| 1261 | Johny Hendricks | Southpaw | Yes | No | Yes | Yes | 56 | 21 | 13 | 8 | 0 | 0 | 5 | 0 | 8 | 3 | 0 | 5 | 3.0 | 1.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 2.0 | 61.904762 | 38.095238 | 23.809524 | 0.000000 | 38.095238 | 14.285714 | 0.000000 | 23.809524 | 33.333333 | 66.666667 | 0.000000 | 0.000000 | 33.333333 | 0.000000 | 0.000000 | 66.666667 | 2.666667 |
| 1262 | Georges St-Pierre | Orthodox | Yes | Yes | Yes | Yes | 71 | 22 | 20 | 2 | 0 | 0 | 5 | 3 | 12 | 1 | 1 | 0 | 15.0 | 13.0 | 2.0 | 0.0 | 0.0 | 3.0 | 2.0 | 8.0 | 1.0 | 1.0 | 0.0 | 90.909091 | 9.090909 | 22.727273 | 13.636364 | 54.545455 | 4.545455 | 4.545455 | 0.000000 | 86.666667 | 13.333333 | 20.000000 | 13.333333 | 53.333333 | 6.666667 | 6.666667 | 0.000000 | 3.227273 |
| 1431 | Frank Mir | Southpaw | Yes | Yes | Yes | Yes | 43 | 27 | 16 | 11 | 0 | 0 | 5 | 8 | 2 | 8 | 0 | 3 | 5.0 | 2.0 | 3.0 | 0.0 | 0.0 | 1.0 | 1.0 | 0.0 | 3.0 | 0.0 | 0.0 | 59.259259 | 40.740741 | 18.518519 | 29.629630 | 7.407407 | 29.629630 | 0.000000 | 11.111111 | 40.000000 | 60.000000 | 20.000000 | 20.000000 | 0.000000 | 60.000000 | 0.000000 | 0.000000 | 1.592593 |
| 1785 | Jon Fitch | Orthodox | No | No | Yes | No | 50 | 18 | 14 | 3 | 1 | 0 | 1 | 3 | 10 | 1 | 0 | 2 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 77.777778 | 16.666667 | 5.555556 | 16.666667 | 55.555556 | 5.555556 | 0.000000 | 11.111111 | 0.000000 | 100.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 100.000000 | 2.777778 |
# Counting the values in the STANCE column
top_1_stance_count = top_1["STANCE"].value_counts()
# Calculating the percentage of each stance
top_1_stance_percentage = top_1["STANCE"].value_counts(normalize=True) * 100
# Combine count and percentage into a single DataFrame for a better display
top_1_stance_summary = pd.DataFrame({'Count': top_1_stance_count, 'Percentage': top_1_stance_percentage})
top_1_stance_summary
| Count | Percentage | |
|---|---|---|
| Southpaw | 8 | 61.538462 |
| Orthodox | 5 | 38.461538 |
top_10 = records[records['FIGHTER'].isin(top_10_percent_list)]
top_10
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | Total_Rounds | Total_Fights | Wins | Losses | Draws | No_Contests | Win_by_KO_TKO | Win_by_Submission | Win_by_Decision | Loss_by_KO_TKO | Loss_by_Submission | Loss_by_Decision | Total_Fights_with_Title | Wins_with_Title | Losses_with_Title | Draws_with_Title | No_Contests_with_Title | Win_by_KO_TKO_with_Title | Win_by_Submission_with_Title | Win_by_Decision_with_Title | Loss_by_KO_TKO_with_Title | Loss_by_Submission_with_Title | Loss_by_Decision_with_Title | Win_% | Loss_% | KO_Win_% | Sub_Win_% | Dec_Win_% | KO_Loss_% | Sub_Loss_% | Dec_Loss_% | Title_Win_% | Title_Loss_% | Title_KO_Win_% | Title_Sub_Win_% | Title_Dec_Win_% | Title_KO_Loss_% | Title_Sub_Loss_% | Title_Dec_Loss_% | Rounds_per_Fight | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5 | Dustin Poirier | Southpaw | No | Yes | Yes | Yes | 71 | 30 | 22 | 7 | 0 | 1 | 11 | 4 | 7 | 3 | 3 | 1 | 3.0 | 1.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 2.0 | 0.0 | 73.333333 | 23.333333 | 36.666667 | 13.333333 | 23.333333 | 10.000000 | 10.000000 | 3.333333 | 33.333333 | 66.666667 | 0.000000 | 0.0 | 33.333333 | 0.000000 | 66.666667 | 0.0 | 2.366667 |
| 17 | Rafael Dos Anjos | Southpaw | Yes | No | Yes | Yes | 111 | 35 | 21 | 14 | 0 | 0 | 4 | 5 | 12 | 3 | 1 | 10 | 4.0 | 2.0 | 2.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 | 1.0 | 0.0 | 1.0 | 60.000000 | 40.000000 | 11.428571 | 14.285714 | 34.285714 | 8.571429 | 2.857143 | 28.571429 | 50.000000 | 50.000000 | 25.000000 | 0.0 | 25.000000 | 25.000000 | 0.000000 | 25.0 | 3.171429 |
| 100 | Brad Tavares | Orthodox | No | No | No | No | 65 | 24 | 15 | 9 | 0 | 0 | 2 | 0 | 13 | 5 | 0 | 4 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 62.500000 | 37.500000 | 8.333333 | 0.000000 | 54.166667 | 20.833333 | 0.000000 | 16.666667 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.708333 |
| 115 | Max Griffin | Orthodox | No | No | No | No | 44 | 16 | 8 | 8 | 0 | 0 | 3 | 0 | 5 | 1 | 0 | 7 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 50.000000 | 50.000000 | 18.750000 | 0.000000 | 31.250000 | 6.250000 | 0.000000 | 43.750000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.750000 |
| 142 | Drew Dober | Southpaw | No | No | No | No | 46 | 23 | 13 | 9 | 0 | 1 | 9 | 1 | 3 | 1 | 4 | 4 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 56.521739 | 39.130435 | 39.130435 | 4.347826 | 13.043478 | 4.347826 | 17.391304 | 17.391304 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.000000 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1912 | Randy Couture | Orthodox | Yes | Yes | Yes | Yes | 57 | 20 | 12 | 8 | 0 | 0 | 5 | 3 | 4 | 7 | 0 | 1 | 14.0 | 8.0 | 6.0 | 0.0 | 0.0 | 5.0 | 0.0 | 3.0 | 6.0 | 0.0 | 0.0 | 60.000000 | 40.000000 | 25.000000 | 15.000000 | 20.000000 | 35.000000 | 0.000000 | 5.000000 | 57.142857 | 42.857143 | 35.714286 | 0.0 | 21.428571 | 42.857143 | 0.000000 | 0.0 | 2.850000 |
| 1918 | Kurt Pellegrino | Orthodox | No | No | No | No | 28 | 12 | 7 | 5 | 0 | 0 | 1 | 4 | 2 | 0 | 2 | 3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 58.333333 | 41.666667 | 8.333333 | 33.333333 | 16.666667 | 0.000000 | 16.666667 | 25.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.333333 |
| 1919 | Ricardo Almeida | Orthodox | No | No | No | No | 25 | 11 | 6 | 5 | 0 | 0 | 0 | 3 | 3 | 1 | 1 | 2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 54.545455 | 45.454545 | 0.000000 | 27.272727 | 27.272727 | 9.090909 | 9.090909 | 18.181818 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.272727 |
| 1973 | Keith Jardine | Orthodox | No | No | No | No | 30 | 13 | 6 | 7 | 0 | 0 | 2 | 0 | 4 | 4 | 0 | 3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 46.153846 | 53.846154 | 15.384615 | 0.000000 | 30.769231 | 30.769231 | 0.000000 | 23.076923 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.307692 |
| 1974 | Chuck Liddell | Orthodox | Yes | No | Yes | Yes | 42 | 20 | 14 | 6 | 0 | 0 | 9 | 0 | 5 | 5 | 0 | 1 | 7.0 | 5.0 | 2.0 | 0.0 | 0.0 | 5.0 | 0.0 | 0.0 | 2.0 | 0.0 | 0.0 | 70.000000 | 30.000000 | 45.000000 | 0.000000 | 25.000000 | 25.000000 | 0.000000 | 5.000000 | 71.428571 | 28.571429 | 71.428571 | 0.0 | 0.000000 | 28.571429 | 0.000000 | 0.0 | 2.100000 |
138 rows × 46 columns
# Counting the values in the STANCE column
top_10_stance_count = top_10["STANCE"].value_counts()
# Calculating the percentage of each stance
top_10_stance_percentage = top_10["STANCE"].value_counts(normalize=True) * 100
# Combine count and percentage into a single DataFrame for a better display
top_10_stance_summary = pd.DataFrame({'Count': top_10_stance_count, 'Percentage': top_10_stance_percentage})
top_10_stance_summary
| Count | Percentage | |
|---|---|---|
| Orthodox | 107 | 77.536232 |
| Southpaw | 28 | 20.289855 |
| Switch | 3 | 2.173913 |
# Remove rows with nulls in 'STANCE' column
cleaned_data = records.dropna(subset=['STANCE'])
# Define color map for stances
color_map = {
'Orthodox': 'red',
'Southpaw': 'blue',
'Switch': 'purple'
}
# Adjusting the figure size for a bigger plot
fig, ax = plt.subplots(figsize=(12, 10)) # Increased figure size
for stance, df_group in cleaned_data.groupby('STANCE'):
for champ, df_champ in df_group.groupby('EITHER_CHAMP'):
marker = '*' if champ == 'Yes' else 'o'
color = color_map.get(stance, 'gray') # Use the defined color map
ax.scatter(df_champ['Win_%'], df_champ['Total_Fights'], label=f"{stance}, Champ: {champ}", color=color, alpha=0.6, marker=marker)
# Customize the plot
ax.set_xlabel('Win Percentage')
ax.set_ylabel('Total Fights')
ax.set_title('Win Percentage vs. Total Fights by Stance and Championship Status')
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)
plt.tight_layout()
plt.show()
# Filter the dataframe for Total_Fights greater than 10
df_ten= cleaned_data[cleaned_data['Total_Fights'] > 10]
# Adjusting the figure size for a bigger plot
fig, ax = plt.subplots(figsize=(12, 10)) # Increased figure size
for stance, df_group in df_ten.groupby('STANCE'):
for champ, df_champ in df_group.groupby('EITHER_CHAMP'):
marker = '*' if champ == 'Yes' else 'o'
color = color_map.get(stance, 'gray') # Use the defined color map
ax.scatter(df_champ['Win_%'], df_champ['Total_Fights'], label=f"{stance}, Champ: {champ}", color=color, alpha=0.6, marker=marker)
# Customize the plot
ax.set_xlabel('Win Percentage')
ax.set_ylabel('Total Fights')
ax.set_title('Win Percentage vs. Total Fights by Stance and Championship Status')
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)
plt.tight_layout()
plt.show()
# Prepare the data for clustering
X = records[['Win_%']].dropna() # Removing rows with NaN in 'Win_%' to avoid errors during clustering
# Elbow method to find the optimal number of clusters
wcss = [] # Within cluster sum of squares
for i in range(1, 11):
kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)
kmeans.fit(X)
wcss.append(kmeans.inertia_)
# Plotting the results onto a line graph to observe the 'elbow'
plt.figure(figsize=(10,5))
plt.plot(range(1, 11), wcss)
plt.title('Elbow Method')
plt.xlabel('Number of clusters')
plt.ylabel('WCSS') # Within cluster sum of squares
plt.grid(True)
plt.show()
# Proceed with K-means clustering using an example choice of 3 clusters
kmeans = KMeans(n_clusters=3, init='k-means++', max_iter=300, n_init=10, random_state=0)
kmeans.fit(X)
# Assigning cluster labels to the data
X['Cluster'] = kmeans.labels_
# Display the first few rows of the dataframe with cluster labels
X.head()
| Win_% | Cluster | |
|---|---|---|
| 0 | 100.000000 | 2 |
| 1 | 50.000000 | 0 |
| 2 | 72.222222 | 2 |
| 3 | 85.714286 | 2 |
| 4 | 71.428571 | 2 |
# Prepare the data for clustering including both 'Win_%' and 'Total_Fights'
X_multi = records[['Win_%', 'Total_Fights']].dropna() # Removing rows with NaN to avoid errors during clustering
# Applying K-means clustering with 3 clusters as a starting example
kmeans_multi = KMeans(n_clusters=3, init='k-means++', max_iter=300, n_init=10, random_state=0)
kmeans_multi.fit(X_multi)
# Assigning cluster labels to the data
X_multi['Cluster'] = kmeans_multi.labels_
# Display the first few rows of the dataframe with cluster labels
X_multi.head()
| Win_% | Total_Fights | Cluster | |
|---|---|---|---|
| 0 | 100.000000 | 1 | 2 |
| 1 | 50.000000 | 6 | 1 |
| 2 | 72.222222 | 18 | 2 |
| 3 | 85.714286 | 7 | 2 |
| 4 | 71.428571 | 7 | 2 |
# Let's visualize the clustering on a scatter plot with 'Win_%' on the x-axis and 'Total_Fights' on the y-axis.
plt.figure(figsize=(12, 8))
# Scatter plot for clusters
for cluster in set(kmeans_multi.labels_):
filtered_data = X_multi[X_multi['Cluster'] == cluster]
plt.scatter(filtered_data['Win_%'], filtered_data['Total_Fights'], label=f'Cluster {cluster}')
plt.title('Clustering of Fighters based on Win_% and Total Fights')
plt.xlabel('Win_%')
plt.ylabel('Total Fights')
plt.legend()
plt.grid(True)
plt.show()
# Calculating the average Total_Fights and Win_% by STANCE
average_stats_by_stance = cleaned_data.groupby('STANCE')[['Total_Fights', 'Win_%']].mean()
average_stats_by_stance
| Total_Fights | Win_% | |
|---|---|---|
| STANCE | ||
| Orthodox | 6.623985 | 35.409997 |
| Southpaw | 7.825000 | 39.094968 |
| Switch | 6.105263 | 36.037242 |
# Calculating the average Total_Fights and Win_% by STANCE
average_stats_by_stance = records.groupby('STANCE')[['Total_Fights', 'Win_%']].mean()
# Grouping by STANCE, summing Total Wins and Total Fights
sum_stats_by_stance = records.groupby('STANCE')[['Wins', 'Total_Fights']].sum()
# Creating a new column 'Win_%' by dividing Wins by Total_Fights in the summed data
sum_stats_by_stance['Win_%'] = (sum_stats_by_stance['Wins'] / sum_stats_by_stance['Total_Fights']) * 100
sum_stats_by_stance
| Wins | Total_Fights | Win_% | |
|---|---|---|---|
| STANCE | |||
| Orthodox | 5145 | 10605 | 48.514851 |
| Southpaw | 1452 | 2817 | 51.544196 |
| Switch | 357 | 696 | 51.293103 |
# Calculate the correlation between Win_% and Total_Fights
correlation = records['Win_%'].corr(records['Total_Fights'])
correlation
0.4608597384904863
# Calculate the correlation of Win_% to Total_Fights for each value in STANCE
correlation_by_stance = records.groupby('STANCE')['Win_%', 'Total_Fights'].corr().iloc[0::2,-1].reset_index()
# Rename columns for clarity
correlation_by_stance.columns = ['STANCE', 'Measurement', 'Correlation']
correlation_by_stance = correlation_by_stance.drop('Measurement', axis=1)
correlation_by_stance
/var/folders/_6/_1qflrx12_j4kc3ssfs46ldw0000gn/T/ipykernel_1785/3902886768.py:2: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
correlation_by_stance = records.groupby('STANCE')['Win_%', 'Total_Fights'].corr().iloc[0::2,-1].reset_index()
| STANCE | Correlation | |
|---|---|---|
| 0 | Orthodox | 0.550111 |
| 1 | Southpaw | 0.517370 |
| 2 | Switch | 0.675876 |
# Filter out rows where 'STANCE' is null and then analyze the relationship between 'STANCE' and 'Wins'
filtered_data = records.dropna(subset=['STANCE'])
# Since 'STANCE' is a categorical variable and 'Wins' is numerical, we'll use one-hot encoding for 'STANCE' to perform correlation analysis
stance_dummies = pd.get_dummies(filtered_data['STANCE'])
# Join the encoded stance data with the 'Wins' column
stance_wins_data = stance_dummies.join(filtered_data['Wins'])
# Calculate correlation between stance types and wins
correlation_matrix = stance_wins_data.corr().loc[['Wins']]
correlation_matrix
| Orthodox | Southpaw | Switch | Wins | |
|---|---|---|---|---|
| Wins | -0.063166 | 0.077987 | -0.013224 | 1.0 |
# For visualization, we'll create a bar plot to show the correlation of each stance with wins.
# Extracting correlation values excluding the last one (Wins with itself)
correlation_values = correlation_matrix.drop(columns=['Wins']).iloc[0]
# Plotting
plt.figure(figsize=(10, 6))
correlation_values.plot(kind='bar', color=['blue', 'green', 'red'])
plt.title('Correlation between Stance and Wins')
plt.xlabel('Stance')
plt.ylabel('Correlation Coefficient')
plt.axhline(0, color='black', linewidth=0.8) # Line to indicate zero correlation
plt.xticks(rotation=45)
plt.grid(axis='y', linestyle='--')
plt.show()
# We need to merge the cluster labels back into the original dataset to associate each fighter with their cluster.
# We'll join on the index after ensuring it's aligned between the datasets.
data_with_clusters = records.join(X_multi['Cluster'], how='inner')
# Now we'll filter out rows where 'STANCE' is null to focus on Orthodox, Southpaw, and Switch only.
data_with_clusters_no_nulls = data_with_clusters.dropna(subset=['STANCE'])
# Group by cluster and stance, then count the number of fighters in each category.
stance_distribution_by_cluster = data_with_clusters_no_nulls.groupby(['Cluster', 'STANCE']).size().unstack(fill_value=0)
stance_distribution_by_cluster
| STANCE | Orthodox | Southpaw | Switch |
|---|---|---|---|
| Cluster | |||
| 0 | 491 | 96 | 36 |
| 1 | 753 | 166 | 49 |
| 2 | 357 | 98 | 29 |
# Calculate the percentage of each stance within each cluster
stance_percentage_by_cluster = stance_distribution_by_cluster.div(stance_distribution_by_cluster.sum(axis=1), axis=0) * 100
stance_percentage_by_cluster
| STANCE | Orthodox | Southpaw | Switch |
|---|---|---|---|
| Cluster | |||
| 0 | 78.812199 | 15.409310 | 5.778491 |
| 1 | 77.789256 | 17.148760 | 5.061983 |
| 2 | 73.760331 | 20.247934 | 5.991736 |
# Hsitograms
data = fb
# Setting up the visualization environment
sns.set(style="whitegrid")
# Creating histograms for numerical columns
fig, ax = plt.subplots(3, 1, figsize=(10, 15))
numerical_cols = ['Buyrate', 'Avg_Buyrate_Per_Event', 'PPV_Fights']
for i, col in enumerate(numerical_cols):
sns.histplot(data[col], bins=30, ax=ax[i], kde=True)
ax[i].set_title(f'Histogram of {col}', fontsize=15)
ax[i].set_xlabel('')
ax[i].set_ylabel('Frequency')
plt.tight_layout()
plt.show()
# Creating box plots for numerical columns
fig, ax = plt.subplots(3, 1, figsize=(10, 15))
for i, col in enumerate(numerical_cols):
sns.boxplot(x=data[col], ax=ax[i])
ax[i].set_title(f'Box Plot of {col}', fontsize=15)
ax[i].set_xlabel('')
plt.tight_layout()
plt.show()
# Cumulative Distribution Function
# Creating CDFs for numerical columns
fig, ax = plt.subplots(3, 1, figsize=(10, 15))
for i, col in enumerate(numerical_cols):
sns.ecdfplot(data[col], ax=ax[i])
ax[i].set_title(f'Cumulative Distribution Function of {col}', fontsize=15)
ax[i].set_xlabel('')
ax[i].set_ylabel('CDF')
plt.tight_layout()
plt.show()
# Scatterplot
# Creating Q-Q plots for numerical columns
fig, ax = plt.subplots(1, 3, figsize=(18, 6))
for i, col in enumerate(numerical_cols):
stats.probplot(data[col].dropna(), dist="norm", plot=ax[i])
ax[i].set_title(f'Q-Q Plot of {col}', fontsize=15)
plt.tight_layout()
plt.show()
# Scatter plot for Buyrate vs. Avg_Buyrate_Per_Event
plt.figure(figsize=(10, 7))
sns.scatterplot(x='Buyrate', y='Avg_Buyrate_Per_Event', data=data)
plt.title('Scatter Plot of Buyrate vs. Avg_Buyrate_Per_Event', fontsize=15)
plt.xlabel('Buyrate')
plt.ylabel('Avg_Buyrate_Per_Event')
plt.show()
# Calculating the correlation matrix
corr_matrix = data[numerical_cols].corr()
# Creating a heatmap to visualize the correlation matrix
plt.figure(figsize=(10, 7))
sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', linewidths=.5)
plt.title('Heat Map of Correlation Between Numerical Variables', fontsize=15)
plt.show()
# Creating a scatterplot matrix for the numerical columns
sns.pairplot(data[numerical_cols].dropna())
plt.suptitle('Scatterplot Matrix for Numerical Variables', size=15, y=1.02)
plt.show()
# Creating a violin chart for Buyrate across different STANCE categories
plt.figure(figsize=(10, 7))
sns.violinplot(x='STANCE', y='Buyrate', data=data)
plt.title('Violin Chart of Buyrate by STANCE', fontsize=15)
plt.xlabel('STANCE')
plt.ylabel('Buyrate')
plt.show()
# Visualizing distributions for numerical and categorical variables
# Setting up the visualization environment
fig, ax = plt.subplots(2, 2, figsize=(14, 12))
# Numerical distributions: Histograms for Buyrate and Avg_Buyrate_Per_Event to show potential skewness
sns.histplot(data['Buyrate'], bins=30, kde=True, ax=ax[0, 0])
ax[0, 0].set_title('Histogram of Buyrate', fontsize=12)
sns.histplot(data['Avg_Buyrate_Per_Event'], bins=30, kde=True, ax=ax[0, 1])
ax[0, 1].set_title('Histogram of Avg_Buyrate_Per_Event', fontsize=12)
# Categorical distribution: Bar plot for STANCE
stance_counts = data['STANCE'].value_counts()
stance_counts.plot(kind='bar', ax=ax[1, 0])
ax[1, 0].set_title('Bar Plot of STANCE Categories', fontsize=12)
ax[1, 0].set_ylabel('Frequency')
# Checking for bimodality: Histogram for PPV_Fights
sns.histplot(data['PPV_Fights'], bins=30, kde=True, ax=ax[1, 1])
ax[1, 1].set_title('Histogram of PPV_Fights', fontsize=12)
plt.tight_layout()
plt.show()
# Filtering the dataset for champions and non-champions based on the 'EVER_CHAMPION' column
champions = data[data['EVER_CHAMPION'] == 'Yes']['Buyrate'].dropna()
non_champions = data[data['EVER_CHAMPION'] == 'No']['Buyrate'].dropna()
# Performing a t-test between the two groups
t_stat, p_value = ttest_ind(champions, non_champions, equal_var=False) # Assuming unequal variances
# Results
t_stat, p_value
(7.543579192229696, 3.8503025640219814e-11)
# Aligning the data by dropping rows where either 'Buyrate' or 'PPV_Fights' is NaN
aligned_data = data.dropna(subset=['Buyrate', 'PPV_Fights'])
# Recalculating the correlation coefficient
correlation_coefficient, p_value_corr = stats.pearsonr(aligned_data['Buyrate'], aligned_data['PPV_Fights'])
correlation_coefficient, p_value_corr
(0.8679961005667428, 8.06575271497532e-252)
# Filtering the data for different stances and their buyrates
stances = data['STANCE'].dropna().unique()
stance_groups = [data[data['STANCE'] == stance]['Buyrate'].dropna() for stance in stances]
# Performing ANOVA
anova_result = f_oneway(*stance_groups)
anova_result.statistic, anova_result.pvalue
(3.075624901955279, 0.04669368569161873)
# Creating a contingency table for EVER_CHAMPION and EVER_INTERIM
contingency_table = pd.crosstab(data['EVER_CHAMPION'], data['EVER_INTERIM'])
# Performing Chi-Square Test for Independence
chi2, p_value_chi, dof, expected = chi2_contingency(contingency_table)
chi2, p_value_chi
(44.36607327242141, 2.7236655263074702e-11)
# Hypothetical population mean
hypothetical_mean = 500000
# Performing a one-sample t-test
t_stat_one_sample, p_value_one_sample = ttest_1samp(data['Buyrate'].dropna(), hypothetical_mean)
t_stat_one_sample, p_value_one_sample
(26.096225616788583, 7.875443393880771e-110)
# Calculate the Test Statistic
# We already have the groups defined: champions vs. non-champions
# Determine the P-Value
# Reusing the two-sample t-test calculation from earlier for the same groups
t_stat_ab, p_value_ab = ttest_ind(champions, non_champions, equal_var=False)
# Displaying the test statistic and p-value as part of the A/B testing steps
t_stat_ab, p_value_ab
(7.543579192229696, 3.8503025640219814e-11)
# First, calculate the total cumulative buyrate to understand the target for 80%
df = fb
total_buyrate = df['Buyrate'].sum()
# Calculate 80% of the total cumulative buyrate
target_buyrate = total_buyrate * 0.8
# Sort the dataframe by 'Buyrate' descending
df_sorted_by_buyrate = df.sort_values(by='Buyrate', ascending=False)
# Calculate the cumulative sum of buyrates
df_sorted_by_buyrate['cumulative_sum'] = df_sorted_by_buyrate['Buyrate'].cumsum()
# Find the smallest number of top buyrates that make up at least 80% of the total
top_buyrates_for_80_percent = df_sorted_by_buyrate[df_sorted_by_buyrate['cumulative_sum'] <= target_buyrate]
top_buyrates_for_80_percent
| FIGHTER | STANCE | EVER_CHAMPION | EVER_INTERIM | EVER_CHALLENGER | EITHER_CHAMP | Buyrate | Avg_Buyrate_Per_Event | PPV_Fights | Buyrate_PERCENTILE | PPV_Fights_PERCENTILE | cumulative_sum | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Conor McGregor | Southpaw | Yes | Yes | Yes | Yes | 13555166.0 | 1.355517e+06 | 10.0 | 100.000000 | 94.283647 | 1.355517e+07 |
| 1 | Georges St-Pierre | Orthodox | Yes | Yes | Yes | Yes | 12430000.0 | 5.650000e+05 | 22.0 | 99.956841 | 99.927641 | 2.598517e+07 |
| 2 | Jim Miller | Southpaw | No | No | No | No | 11741000.0 | 6.522778e+05 | 18.0 | 99.913681 | 99.493488 | 3.772617e+07 |
| 3 | Jon Jones | Orthodox | Yes | Yes | Yes | Yes | 10992000.0 | 6.465882e+05 | 17.0 | 99.870522 | 99.204052 | 4.871817e+07 |
| 4 | Demian Maia | Southpaw | No | No | Yes | No | 10907000.0 | 5.453500e+05 | 20.0 | 99.827363 | 99.710564 | 5.962517e+07 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 417 | Igor Pokrajac | Orthodox | No | No | No | No | 1690000.0 | 3.380000e+05 | 5.0 | 81.981010 | 81.150507 | 1.516299e+09 |
| 418 | Dan Hardy | Orthodox | No | No | Yes | No | 1690000.0 | 5.633333e+05 | 3.0 | 81.981010 | 65.484805 | 1.517989e+09 |
| 419 | Marlon Vera | Switch | No | No | Yes | No | 1685000.0 | 4.212500e+05 | 4.0 | 81.916271 | 74.963821 | 1.519674e+09 |
| 420 | Liana Jojua | Orthodox | No | No | No | No | 1675000.0 | 8.375000e+05 | 2.0 | 81.873112 | 49.963821 | 1.521349e+09 |
| 421 | David Heath | Orthodox | No | No | No | No | 1670000.0 | 5.566667e+05 | 3.0 | 81.829953 | 65.484805 | 1.523019e+09 |
414 rows × 12 columns